Group Project 2
Knut Gjertsen
12/3/2017
Libraries and Data Setup
## Warning: package 'dplyr' was built under R version 3.4.2Data Exploration:
Structure of the Dataset:
The dataset includes a large amount of variables. Some of these variables are aggregate variables (made from a combination of other variables) and some do not make sense to include in the prediction model from a domain perspective. Also, some variables may not be relevant to our prediciton. After exploration and cleaning, we can use logistic regression to remove the variables with little to no influence.
str(listings)## 'data.frame': 33300 obs. of 51 variables:
## $ number_of_days : num 1095 1095 1095 296 1095 ...
## $ principal_balance : num 969 0 0 3636 0 ...
## $ loan_status : num 0 1 0 1 0 0 0 0 0 0 ...
## $ loan_status_description : Factor w/ 4 levels "CHARGEOFF","COMPLETED",..: 3 4 2 1 2 2 3 2 2 2 ...
## $ loan_origination_date : Factor w/ 250 levels "1/10/2013","1/11/2013",..: 14 14 14 14 17 17 17 17 17 17 ...
## $ amount_funded : num 2000 2000 2000 4000 5500 10000 6500 3000 4000 2200 ...
## $ prosper_rating : Factor w/ 7 levels "A","AA","B","C",..: 3 5 6 7 1 3 3 5 5 5 ...
## $ borrower_rate : num 16.9 24.2 27.9 31.3 12.5 ...
## $ listing_term : int 60 36 36 36 36 36 36 36 60 60 ...
## $ listing_monthly_payment : num 49.6 78.7 82.6 172.8 184 ...
## $ scorex : Factor w/ 11 levels "< 600","600-619",..: 8 5 3 4 10 6 11 8 6 5 ...
## $ prosper_score : int 7 4 3 7 7 7 5 3 3 6 ...
## $ listing_category_id : int 13 13 1 2 1 1 1 1 1 1 ...
## $ income_range : int 4 6 2 2 4 3 6 5 4 4 ...
## $ income_range_description : Factor w/ 7 levels "$0 ","$1-24,999",..: 5 3 2 2 5 4 3 6 5 5 ...
## $ stated_monthly_income : num 4250 10833 1656 2000 4315 ...
## $ income_verifiable : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ dti_wprosper_loan : num 1.8e-01 2.0e-01 1.1e-01 9.6e-01 1.3e-01 2.9e-01 1.0e+06 1.7e-01 4.4e-01 2.5e-01 ...
## $ employment_status_description: Factor w/ 7 levels "Employed","Full-time",..: 1 1 4 2 1 1 7 1 1 1 ...
## $ occupation : Factor w/ 68 levels "","Accountant/CPA",..: 37 37 37 50 14 61 21 37 37 21 ...
## $ months_employed : int 56 24 74 132 79 39 40 373 196 199 ...
## $ borrower_state : Factor w/ 48 levels "AK","AL","AR",..: 16 43 21 41 5 10 14 12 33 21 ...
## $ borrower_city : Factor w/ 6531 levels "7044","AARONSBURG",..: 4390 2019 332 2627 3881 3327 6305 3648 394 2232 ...
## $ lender_indicator : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthly_debt : int 704 2037 106 1738 392 597 3360 1121 2149 1040 ...
## $ current_delinquencies : int 0 0 17 0 0 0 0 0 0 0 ...
## $ delinquencies_last7_years : int 0 7 3 0 0 0 0 0 11 0 ...
## $ public_records_last10_years : int 1 1 0 0 0 0 0 0 0 0 ...
## $ public_records_last12_months : int 0 0 0 0 0 0 0 0 0 0 ...
## $ first_recorded_credit_line : Factor w/ 9041 levels "1/1/1951","1/1/1957",..: 8521 4007 285 3965 4677 8689 2461 5611 4338 1307 ...
## $ credit_lines_last7_years : int 23 43 37 36 9 10 38 19 57 50 ...
## $ inquiries_last6_months : int 0 4 0 0 1 0 0 0 1 0 ...
## $ amount_delinquent : int 0 0 48150 0 0 0 0 0 0 0 ...
## $ current_credit_lines : int 11 7 3 23 4 9 16 6 20 15 ...
## $ open_credit_lines : int 9 7 3 22 4 9 14 6 18 12 ...
## $ bankcard_utilization : num 0.32 0.17 0.34 0.89 0.26 0.69 0.48 0.67 0.66 0.99 ...
## $ total_open_revolving_accounts: int 7 2 2 18 3 3 12 7 13 7 ...
## $ installment_balance : int 7733 77425 51997 15501 172 19623 24412 0 35987 13554 ...
## $ real_estate_balance : int 0 0 0 49084 0 0 342034 0 234434 0 ...
## $ revolving_balance : int 5106 361 519 24586 6266 3296 38834 48702 27035 6035 ...
## $ real_estate_payment : int 0 0 0 488 0 0 4553 0 1988 0 ...
## $ revolving_available_percent : int 70 86 65 19 74 52 52 33 27 13 ...
## $ total_inquiries : int 1 14 6 3 2 3 0 1 4 2 ...
## $ total_trade_items : int 23 43 37 36 9 10 38 19 57 50 ...
## $ satisfactory_accounts : int 20 32 15 34 9 10 31 19 53 49 ...
## $ now_delinquent_derog : int 0 0 17 0 0 0 0 0 0 0 ...
## $ was_delinquent_derog : int 3 11 5 2 0 0 7 0 4 1 ...
## $ delinquencies_over30_days : int 6 10 4 2 0 0 11 0 5 6 ...
## $ delinquencies_over60_days : int 0 3 3 0 0 0 2 0 4 2 ...
## $ delinquencies_over90_days : int 0 7 3 0 0 0 0 0 11 0 ...
## $ is_homeowner : logi FALSE TRUE FALSE TRUE FALSE FALSE ...The variables that we can remove after exploration are:
- income range
- income_range_description
- loan_status_description
- loan origination date
- Listing monthly payment (Can be calculated)
- Occupation: High number of factors
Analysis of Amount Funded
We begin by examining the distribution of amount funded for each succesful loan. 
Here we plot the count for each amount funded. We find that the majority of the people are looking for smaller loans, and it trails out towards the upper limits of ~35,000. Another interesting thing to note is the spikes at each interval of 5000. ### Analysis of income range description
## Warning: Removed 4 rows containing non-finite values (stat_count).
Here we plot the distribution of the income ranges for the borrowers. We find that it does not necessarily follow a normal distribution, with the majority of the borrows have less than 75,000.
For predictive purposes we will go ahead and assume that incomplete loans which are paid per schedule will be completed per schedule. Similarly, we will convert loans which are sold off to colleciton agencies as defaulted.
Time Analysis
We look to explore how the the different variables differ over time.
Analysis per Quarter
We will start by analysing the potential variation in the amount of loans taken and proportion of defaults among financial quarters. 
This bar chart is highly interesting, as it seems as if people tend to increasingly take out loans as the year progresses.
Analysis By Monthly Quarter Exploration
Additionally, it seems as if the rate of default is lower as the year progresses, especially in the last financial quarter. To test this hypothesis we will calculate and plot the actual numbers for proportion of default.
## [1] 0.1611894## [1] 0.1383878## [1] 0.1266747## [1] 0.1180214
Here it becomes entirely clear to us that the rate of default substantially decreases with each financial quarter. This is a valuable finding which calls for further exploration.
Default by Month Analysis
We will proceed by redoing the previous analysis per month. This could potentially show us whether certain months are drivers of the increased amount of loans and decreased default rates. 
It appears as if there is near linear growth from January until September, which is quite interesting in itself. Additionally, for November and December there appears to be a non-proportional growth in default rates, which could be an option for arbitrage.
Let’s continue by calculating the default rates. 
As suspected, the default rates of especially November and December are substantially lower than other months.
Default Rate vs Borrow Rate vs Month: Arbitrage Opportunity
However, to check whether an investor would be able to find opportunity for arbitrage here, one would need to measure up the rate of default to the interest rate of loans. It could be that the months of November and December are simply filled with ideal loan candidates, demanding low borrowing rates. Let’s try to roughly estimate how the two match up over the year. 
It here becomes clear to us that there does not seem to be a significant enough drop in borrowing rate for November and December to divert from investing in November and December. In conclusion, we believe to have found significant arbitrage opportunities for future investing.
Analysis by Day of Week
As our previous analysis proved quite succesful, we will look towards further analysis how time might impact the default rate. This time we will further investigate the difference between weekdays and weekends.
We will start by labelling our date columns the name of each day.
## Sun Mon Tues Wed Thurs Fri Sat
## 0 5593 7293 7506 6355 6553 0After splitting up and running a summation command we can see that the data set weirdly enough does not appear to include any Saturdays or Sundays. Upon re-inspecting the initial dataset, we could confirm that this was indeed the case.
We then proceed by comparing the different weekdays to each other. We start by investigating the difference in the rate of default.
tapply(listings$loan_status, listings$day, mean)## Sun Mon Tues Wed Thurs Fri Sat
## NA 0.1260504 0.1290278 0.1316280 0.1323367 0.1246757 NAThe results show that there does not seem to be a significant difference in rate of default varying between weekdays.
Let’s run it for a few other variables to see if we can find any interesting results.
tapply(listings$borrower_rate, listings$day, mean)## Sun Mon Tues Wed Thurs Fri Sat
## NA 18.32823 18.22340 18.37695 18.41558 18.28949 NAtapply(listings$amount_funded, listings$day, mean)## Sun Mon Tues Wed Thurs Fri Sat
## NA 10707.98 10504.51 10485.24 10576.31 10567.49 NAtapply(listings$income_range, listings$day, mean)## Sun Mon Tues Wed Thurs Fri Sat
## NA 4.220454 4.243933 4.248335 4.255862 4.241874 NAtapply(listings$income_verifiable, listings$day, mean)## Sun Mon Tues Wed Thurs Fri Sat
## NA 0.9309852 0.9362402 0.9385825 0.9345397 0.9420113 NAtapply(listings$monthly_debt, listings$day, mean)## Sun Mon Tues Wed Thurs Fri Sat
## NA 1094.321 1096.166 1110.861 1100.307 1110.160 NADisappoingly, there does not seem to be any significant differences between weekdays for these variables. We will thus not conduct further investigatiom into the difference between weekdays.
Analysis By Scorex
Scorex is the credit score of the user, it goes from < 600, to 778+. 
Default by Scorex 
Scorex: As the scorex increases, the likelihood of defaulting decreases.
Prosper Score is a score from 1-11 with 1 being lowest risk. Because this is a score that we did not create we don’t know what went into it. In addition the data they are using is probably the same data we are using so it does not provide any novel new information to the model. Lets remove it.
listings$prosper_score <- NULLlistings category ID is a category of the Loan but there is no description to go along with the IDs. This information might be useful though. NNot sure what to do with this.
Income range, Income Range Description, Stated Monthly Income. We don’t want this datapoint to be overrepresented in the model. Therefore we only need one of these variables. Lets go with Stated Monthly Income since it is more granular than the broad income range. TO DO: Some of the algorithms will find that they are highly correlated and won’t “Count them as double”… which ones?
Analysis by Stated Monthly Income
## num [1:33300] 4250 10833 1656 2000 4315 ...## Warning: Removed 22 rows containing non-finite values (stat_bin).
Here we plot a histrogram of the stated monthly income for each borrower. *** analyze
Analyis of Income Verifiable
Income Verifiable, looks good and useful. Have to modify to be a factor first. Will be interested to see if it being verified has impact on default. 
We find that there are many more people with their income verfied then not.
Analysis by Employment Status Description & Employment.
There are blank values for occupation when someone chooses “Other for Employment Status Description”. Lets fix this.
## Factor w/ 7 levels "Employed","Full-time",..: 1 1 4 2 1 1 7 1 1 1 ...## [1] "Employed" "Full-time" "Not employed" "Other"
## [5] "Part-time" "Retired" "Self-employed"
The majority are employed, self employed or other.
Months employed had values less than 0, we remove them and set it back to the median month.
#
listings$months_employed[listings$months_employed <0] <- NA
listings$months_employed[is.na(listings$months_employed)] = median(listings$months_employed, na.rm=TRUE)Feature Engineering and Geographic Exploration
Since the data provideds use with borrow state, it would be interesting to explore the distribution of the geographic location.
States by count of borrowers
We find that california is the most popular state, followed by texas and new york. This makes sense since they are the biggest states.
States by Proportion of Default
A look at the probability of default by states. We find that **
Cost of Living By State
Since each state has a varying costs of living, we thought it would be interesting to explore if this has influence on the probability of default. We found a dataset bytheCouncil for Community & Economic Research (C2ER) that ranks each state by their Cost of living.
## `geom_smooth()` using method = 'loess'
As the Cost of Living for each state rank goes up (less costly), likely hood of default goes down.
Analysis by Borrower Rate
## `geom_smooth()` using method = 'loess'
Income Range: As the scorex increases, the likelihood of defaulting decreases. ** Income Verifiable: Income without being verified are less likely of being defaulted. This is somewhat different than what we expected By listing term: The longer the listing term, the more likely a borrower is to default.
Top 500 cities by GPD Analysis
Another aspect of location that we wanted to explore was cities. Since there were so many cities, we realized we needed to reduce this to a more measurable aspect. To do this, we took a dataset from Bureau of Economic Analysis which provided a list of the top 500 metropolotian areas with the best GDP. We parsed the cities from this and then created a new factor that marks each city as either major GDP or not major GDP.
After separating the cities into two groups, either in the top 500 gpd producing metro’s or not major, we find little correlation.
Data Cleaning
As part of our exploration of our dataset, we find several variables to be irrelevant to further analysis. The variables deleted are found irrelevant as they are aggregate variables or identical variables.
Aggregate Variable of delinquencies
listings$delinq_agg <- listings$delinquencies_over30_days + 2*listings$delinquencies_over60_days + 4*listings$delinquencies_over90_daysFinal Cleaing of Variables
#drop high factor level
#listingssample$borrower_city <- NULL
#listingssample$borrower_state <- NULL
# TODO: convert first_recorded to date
listings$borrower_rate <- NULL
listings$number_of_days <- NULL
listings$principal_balance <- NULL
listings$loan_status_description <- NULL
listings$loan_origination_date <- NULL
listings$listing_monthly_payment <- NULL
listings$listing_category_id <- NULL
listings$income_range <-NULL
listings$income_range_description <-NULL
listings$first_recorded_credit_line <- NULL
<<<<<<< Updated upstream
listings$months <- as.factor(listings$months)
listings$is_homeowner <-as.factor(listings$is_homeowner)
levels(listings$employment_status_description) <- c("Employed", "Fulltime", "NotEmployed", "other", "Parttime", "Retired", "SelfEmployed" )
=======
listings$months <- as.factor(listings$months)
listings$is_homeowner <-as.factor(listings$is_homeowner)
levels(listings$employment_status_description) <- c("Employed", "Fulltime", "NotEmployed", "other", "Parttime", "Retired", "SelfEmployed" )
>>>>>>> Stashed changes
listingsample<- listings[, !(colnames(listings) %in% c("State", "region", "originaldate", "borrower_city", "borrower_state", "occupation", "Rank", "delinquencies_over30_days", "delinquencies_over90_days", "delinquencies_over60_days"))]
#drop aggregates
#remove borrow_rate because its not includede in the bidding
listing_no_agg<- listingsample[, !(colnames(listingsample) %in% c("scorex", "dti_wprosper_loan", "prosper_rating"))]Data Models
<<<<<<< Updated upstreamData Prediction using Logistic Regression
logReg <- glm(loan_status ~ ., data = listingsample, family = "binomial")## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(logReg)##
## Call:
## glm(formula = loan_status ~ ., family = "binomial", data = listingsample)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0592 -0.5818 -0.4608 -0.3332 3.7997
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 7.690e-01 5.063e-01 1.519
## amount_funded 3.325e-05 3.668e-06 9.065
## prosper_ratingAA -8.063e-01 1.674e-01 -4.816
## prosper_ratingB 3.086e-01 6.867e-02 4.494
## prosper_ratingC 7.027e-01 6.717e-02 10.461
## prosper_ratingD 1.013e+00 7.491e-02 13.526
## prosper_ratingE 1.275e+00 8.140e-02 15.660
## prosper_ratingHR 1.555e+00 1.065e-01 14.606
## listing_term 7.971e-03 1.500e-03 5.314
## scorex -4.365e-03 6.046e-04 -7.220
## stated_monthly_income -7.784e-05 7.966e-06 -9.771
## income_verifiableTRUE -3.451e-01 1.196e-01 -2.886
## dti_wprosper_loan 1.562e-03 2.377e-03 0.657
## employment_status_descriptionFulltime -1.066e-02 1.614e-01 -0.066
## employment_status_descriptionNotEmployed -3.123e-01 3.295e-01 -0.948
## employment_status_descriptionother 1.876e-01 7.120e-02 2.635
## employment_status_descriptionParttime 1.602e-01 1.164e+00 0.138
## employment_status_descriptionRetired 1.142e-01 6.394e-01 0.179
## employment_status_descriptionSelfEmployed -1.205e-01 1.359e-01 -0.886
## months_employed -5.747e-04 1.815e-04 -3.166
## lender_indicator -1.760e-01 8.808e-02 -1.998
## monthly_debt 1.073e-04 3.438e-05 3.121
## current_delinquencies -1.871e-01 3.517e-01 -0.532
## delinquencies_last7_years -6.001e-03 9.504e-03 -0.631
## public_records_last10_years 2.226e-02 2.541e-02 0.876
## public_records_last12_months 4.337e-02 9.378e-02 0.462
## credit_lines_last7_years 1.686e-01 3.502e-01 0.482
## inquiries_last6_months 3.727e-02 1.595e-02 2.337
## amount_delinquent 9.913e-07 2.189e-06 0.453
## current_credit_lines -2.644e-03 1.348e-02 -0.196
## open_credit_lines -4.643e-03 1.536e-02 -0.302
## bankcard_utilization -1.949e-01 1.075e-01 -1.813
## total_open_revolving_accounts 1.376e-02 8.619e-03 1.596
## installment_balance 8.477e-07 5.485e-07 1.546
## real_estate_balance 4.733e-08 2.094e-07 0.226
## revolving_balance -4.531e-07 8.522e-07 -0.532
## real_estate_payment -7.548e-06 1.761e-05 -0.429
## revolving_available_percent 2.793e-03 1.334e-03 2.095
## total_inquiries 1.110e-02 5.488e-03 2.023
## total_trade_items NA NA NA
## satisfactory_accounts -1.719e-01 3.502e-01 -0.491
## now_delinquent_derog NA NA NA
## was_delinquent_derog -1.969e-01 3.501e-01 -0.562
## is_homeownerTRUE 8.810e-02 4.817e-02 1.829
## quarters -1.618e-01 1.777e-01 -0.911
## months10 2.608e-01 5.362e-01 0.486
## months11 2.087e-01 5.450e-01 0.383
## months12 6.640e-02 5.453e-01 0.122
## months2 1.415e-01 1.349e-01 1.049
## months3 5.080e-02 1.254e-01 0.405
## months4 3.096e-02 2.159e-01 0.143
## months5 1.378e-01 2.132e-01 0.646
## months6 8.523e-02 2.128e-01 0.400
## months7 1.857e-01 3.700e-01 0.502
## months8 2.158e-01 3.739e-01 0.577
## months9 7.088e-02 3.739e-01 0.190
## day.L -8.834e-03 3.945e-02 -0.224
## day.Q -4.533e-02 3.822e-02 -1.186
## day.C -1.252e-02 3.767e-02 -0.332
## day^4 -4.601e-03 3.614e-02 -0.127
## is_major_gdpNot Major 5.866e-02 3.504e-02 1.674
## delinq_agg 1.023e-03 2.113e-03 0.484
## Pr(>|z|)
## (Intercept) 0.12878
## amount_funded < 2e-16 ***
## prosper_ratingAA 1.46e-06 ***
## prosper_ratingB 6.98e-06 ***
## prosper_ratingC < 2e-16 ***
## prosper_ratingD < 2e-16 ***
## prosper_ratingE < 2e-16 ***
## prosper_ratingHR < 2e-16 ***
## listing_term 1.07e-07 ***
## scorex 5.20e-13 ***
## stated_monthly_income < 2e-16 ***
## income_verifiableTRUE 0.00391 **
## dti_wprosper_loan 0.51124
## employment_status_descriptionFulltime 0.94737
## employment_status_descriptionNotEmployed 0.34315
## employment_status_descriptionother 0.00843 **
## employment_status_descriptionParttime 0.89059
## employment_status_descriptionRetired 0.85828
## employment_status_descriptionSelfEmployed 0.37535
## months_employed 0.00155 **
## lender_indicator 0.04569 *
## monthly_debt 0.00181 **
## current_delinquencies 0.59468
## delinquencies_last7_years 0.52774
## public_records_last10_years 0.38110
## public_records_last12_months 0.64377
## credit_lines_last7_years 0.63006
## inquiries_last6_months 0.01943 *
## amount_delinquent 0.65068
## current_credit_lines 0.84449
## open_credit_lines 0.76251
## bankcard_utilization 0.06983 .
## total_open_revolving_accounts 0.11050
## installment_balance 0.12218
## real_estate_balance 0.82118
## revolving_balance 0.59494
## real_estate_payment 0.66812
## revolving_available_percent 0.03621 *
## total_inquiries 0.04307 *
## total_trade_items NA
## satisfactory_accounts 0.62355
## now_delinquent_derog NA
## was_delinquent_derog 0.57378
## is_homeownerTRUE 0.06744 .
## quarters 0.36248
## months10 0.62669
## months11 0.70171
## months12 0.90309
## months2 0.29424
## months3 0.68540
## months4 0.88598
## months5 0.51806
## months6 0.68885
## months7 0.61577
## months8 0.56391
## months9 0.84965
## day.L 0.82284
## day.Q 0.23550
## day.C 0.73955
## day^4 0.89869
## is_major_gdpNot Major 0.09417 .
## delinq_agg 0.62819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25592 on 33299 degrees of freedom
## Residual deviance: 24157 on 33240 degrees of freedom
## AIC: 24277
##
## Number of Fisher Scoring iterations: 6Comments -
the following variables are stastically significant and have a fairly strong impact on on our response variable, loan_status -
1. amount_funded
2. listing_term
3. scorex
4. stated_monthly_income
5. income_verifiableTRUE
6. employment_status_descriptionother
7. months_employed
8. monthly_debt
9. inquiries_last6_months
10. total_open_revolving_accounts
11. installment_balance
Most of these are summary variables and their presence masks the impact other variables can have on loan_status.
As a result, we will run another regression without these summary variables
logReg_revised <- glm(loan_status ~ . ,
data = listing_no_agg, family = "binomial")## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(logReg_revised)##
## Call:
## glm(formula = loan_status ~ ., family = "binomial", data = listing_no_agg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.8409 -0.5675 -0.4891 -0.3921 4.2618
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -1.108e+00 2.707e-01 -4.093
## amount_funded 3.700e-06 3.362e-06 1.101
## listing_term 1.104e-02 1.450e-03 7.611
## stated_monthly_income -1.054e-04 8.123e-06 -12.981
## income_verifiableTRUE -3.774e-01 1.186e-01 -3.182
## employment_status_descriptionFulltime -9.763e-02 1.578e-01 -0.619
## employment_status_descriptionNotEmployed 1.649e-01 3.238e-01 0.509
## employment_status_descriptionother 1.752e-01 7.025e-02 2.494
## employment_status_descriptionParttime 6.089e-02 1.106e+00 0.055
## employment_status_descriptionRetired -1.274e-01 6.345e-01 -0.201
## employment_status_descriptionSelfEmployed -1.851e-01 1.350e-01 -1.371
## months_employed -7.309e-04 1.803e-04 -4.055
## lender_indicator -4.337e-01 8.448e-02 -5.134
## monthly_debt 2.177e-04 3.955e-05 5.504
## current_delinquencies -2.742e-01 3.537e-01 -0.775
## delinquencies_last7_years -1.340e-02 9.400e-03 -1.426
## public_records_last10_years 5.210e-02 2.433e-02 2.141
## public_records_last12_months 4.459e-02 9.140e-02 0.488
## credit_lines_last7_years 3.048e-01 3.525e-01 0.865
## inquiries_last6_months 1.184e-01 1.517e-02 7.807
## amount_delinquent 2.987e-06 2.193e-06 1.362
## current_credit_lines 2.546e-03 1.343e-02 0.190
## open_credit_lines -1.310e-02 1.528e-02 -0.857
## bankcard_utilization 1.204e-01 1.082e-01 1.112
## total_open_revolving_accounts 3.180e-02 8.575e-03 3.709
## installment_balance 1.471e-06 5.525e-07 2.662
## real_estate_balance -3.895e-07 2.354e-07 -1.655
## revolving_balance -2.215e-06 9.381e-07 -2.361
## real_estate_payment -1.052e-05 2.464e-05 -0.427
## revolving_available_percent -5.333e-03 1.271e-03 -4.196
## total_inquiries 3.029e-02 5.236e-03 5.785
## total_trade_items NA NA NA
## satisfactory_accounts -3.156e-01 3.525e-01 -0.895
## now_delinquent_derog NA NA NA
## was_delinquent_derog -3.100e-01 3.524e-01 -0.880
## is_homeownerTRUE -5.464e-02 4.795e-02 -1.139
## quarters -1.825e-01 1.758e-01 -1.038
## months10 2.785e-01 5.303e-01 0.525
## months11 1.559e-01 5.392e-01 0.289
## months12 1.052e-02 5.395e-01 0.020
## months2 1.577e-01 1.335e-01 1.181
## months3 -1.915e-02 1.242e-01 -0.154
## months4 -1.137e-02 2.137e-01 -0.053
## months5 3.503e-02 2.109e-01 0.166
## months6 -6.265e-03 2.106e-01 -0.030
## months7 9.866e-02 3.661e-01 0.270
## months8 8.167e-02 3.699e-01 0.221
## months9 5.868e-02 3.699e-01 0.159
## day.L -9.141e-03 3.909e-02 -0.234
## day.Q -4.651e-02 3.785e-02 -1.229
## day.C -1.714e-02 3.729e-02 -0.460
## day^4 -3.568e-03 3.577e-02 -0.100
## is_major_gdpNot Major 6.242e-02 3.471e-02 1.798
## delinq_agg 2.765e-03 2.084e-03 1.327
## Pr(>|z|)
## (Intercept) 4.25e-05 ***
## amount_funded 0.271000
## listing_term 2.71e-14 ***
## stated_monthly_income < 2e-16 ***
## income_verifiableTRUE 0.001462 **
## employment_status_descriptionFulltime 0.536116
## employment_status_descriptionNotEmployed 0.610424
## employment_status_descriptionother 0.012644 *
## employment_status_descriptionParttime 0.956109
## employment_status_descriptionRetired 0.840898
## employment_status_descriptionSelfEmployed 0.170359
## months_employed 5.02e-05 ***
## lender_indicator 2.83e-07 ***
## monthly_debt 3.71e-08 ***
## current_delinquencies 0.438178
## delinquencies_last7_years 0.153877
## public_records_last10_years 0.032244 *
## public_records_last12_months 0.625625
## credit_lines_last7_years 0.387213
## inquiries_last6_months 5.85e-15 ***
## amount_delinquent 0.173250
## current_credit_lines 0.849590
## open_credit_lines 0.391210
## bankcard_utilization 0.266104
## total_open_revolving_accounts 0.000208 ***
## installment_balance 0.007778 **
## real_estate_balance 0.097942 .
## revolving_balance 0.018206 *
## real_estate_payment 0.669449
## revolving_available_percent 2.72e-05 ***
## total_inquiries 7.26e-09 ***
## total_trade_items NA
## satisfactory_accounts 0.370560
## now_delinquent_derog NA
## was_delinquent_derog 0.379119
## is_homeownerTRUE 0.254530
## quarters 0.299323
## months10 0.599549
## months11 0.772519
## months12 0.984436
## months2 0.237453
## months3 0.877411
## months4 0.957566
## months5 0.868084
## months6 0.976267
## months7 0.787535
## months8 0.825252
## months9 0.873944
## day.L 0.815113
## day.Q 0.219191
## day.C 0.645690
## day^4 0.920544
## is_major_gdpNot Major 0.072159 .
## delinq_agg 0.184532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25592 on 33299 degrees of freedom
## Residual deviance: 24791 on 33248 degrees of freedom
## AIC: 24895
##
## Number of Fisher Scoring iterations: 5Comments -
After removing the summary variable, we know that the following variables impact loan_status
1. amount_funded
2. listing_term
3. scorex
4. stated_monthly_income
5. income_verifiableTRUE
6. employment_status_descriptionother
7. months_employed
8. monthly_debt
9. inquiries_last6_months
10. total_open_revolving_accounts
11. installment_balance
Most of these are summary variables and their presence masks the impact other variables can have on loan_status.
As a result, we will run another regression without these summary variables
Getting Data Ready for Machine Learning Models
Since our dataset is relatively small ~33,300 after cleaning and the proportion of our predictor variable is low ~13%, we refine our dataset to have a more equal distribution of the two outcomes. This will help prevent overfitting or poor classification results. From our datset, about 4292 default and 29008 do not default.
default_listings <- listingsample[listingsample$loan_status == 1, ]
no_default_listings <- listingsample[listingsample$loan_status == 0, ]
index <- sample(1:nrow(no_default_listings), 8600)
no_default_listings_sample <- no_default_listings[index, ]
listingsample <- rbind(no_default_listings_sample, default_listings)# Randomizing dataset
set.seed(12345)
listing_rand <- listingsample[order(runif(12892)),]
#listing_rand$loan_status <- as.factor(listing_rand$loan_status)
loan_status <- listing_rand[1]
l_dummy <-model.matrix(~ . -1, data = listing_rand[-1])
l_dummy <- as.data.frame(scale(l_dummy))
l_dummy<- cbind(loan_status, l_dummy)
#normalize <- function(x) {
# return ((x - min(x)) / (max(x) - min(x)))
#}
#l_dummy <- as.data.frame(lapply(l_dummy, normalize))
#l_dummy<- cbind(loan_status, l_dummy)
#library(plyr)
#l_dummy<- rename(l_dummy, c("beta"="two", "gamma"="three"))
names(l_dummy)[names(l_dummy)=="scorex< 600"] <- "scorexLess"
names(l_dummy)[names(l_dummy)=="scorex600-619"] <- "sx619"
names(l_dummy)[names(l_dummy)=="scorex620-639"] <- "sx639"
names(l_dummy)[names(l_dummy)=="scorex640-649"] <- "sx649"
names(l_dummy)[names(l_dummy)=="scorex650-664"] <- "sx664"
names(l_dummy)[names(l_dummy)=="scorex665-689"] <- "sx689"
names(l_dummy)[names(l_dummy)=="scorex690-701"] <- "sx701"
names(l_dummy)[names(l_dummy)=="scorex702-723"] <- "sx723"
names(l_dummy)[names(l_dummy)=="scorex724-747"] <- "sx747"
names(l_dummy)[names(l_dummy)=="scorex748-777"] <- "sx777"
names(l_dummy)[names(l_dummy)=="scorex778+"] <- "sx778"
names(l_dummy)[names(l_dummy)=="day^4"] <- "day4"
names(l_dummy)[names(l_dummy)=="day^5"] <- "day5"
names(l_dummy)[names(l_dummy)=="day^6"] <- "day6"
names(l_dummy)[names(l_dummy)=="top_ten_gdpNot Major"] <- "nogdp"
train_data_indicies <- sample(1:nrow(l_dummy),
replace = F,
size = floor(nrow(l_dummy) * 0.8))
train_data <- l_dummy[train_data_indicies, ]
test_data <- l_dummy[-train_data_indicies, ]
#listings_random <- listings[sample(nrow(listings)),]
round(prop.table(table(train_data$loan_status)) * 100, 1)##
## 0 1
## 67 33round(prop.table(table(test_data$loan_status)) * 100, 1)##
## 0 1
## 65.6 34.4#listing_formula <- colnames(train_data) %>%
# {paste(.[! . %in% "loan_status"], collapse = " + ")} %>%
# paste("loan_status ~ ", .) %>%
# as.formula()accuracy function
accuracy <- function(predicted, trueval, model, hideoutput = F) {
stopifnot(length(predicted) == length(trueval))
result <- sum(predicted == trueval) / length(predicted)
if (!hideoutput) {cat("Model:", model, "had", result, "accuracy\n")}
return(result)
}#train_data$loan_status <- as.factor(train_data$loan_status)Support Vector Machines - vanilladot
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:ggplot2':
##
## alpha# Needs to be in a factor for SVM to be treated as a classification
train_data$loan_status <- as.factor(train_data$loan_status)
train_data$loan_status <- as.factor(train_data$loan_status)
# vanilladot - Implement Model
classifier_vanilladot <- ksvm(loan_status ~ ., data = train_data, kernel = "vanilladot")## Setting default kernel parameters# vanilladot - Make Prediction
prediction_vanilladot <- predict(classifier_vanilladot, test_data)
# vanilladot - See some results
prop.table(table(prediction_vanilladot, test_data$loan_status))##
## prediction_vanilladot 0 1
## 0 0.64094610 0.32221791
## 1 0.01512214 0.02171384# vanilladot - Evaluate overall efficency
prop.table(table(prediction_vanilladot == test_data$loan_status))##
## FALSE TRUE
## 0.3373401 0.6626599a8 = accuracy(prediction_vanilladot, test_data$loan_status, "Vanilla SVM", TRUE)SVM - rbfdot
library(kernlab)
# Needs to be in a factor for SVM to be treated as a classification
# rbfdot
classifier_rbfdot <- ksvm(loan_status ~ ., data = train_data, kernel = "rbfdot")
prediction_rbfdot <- predict(classifier_rbfdot, test_data)
#See some results
prop.table(table(prediction_rbfdot, test_data$loan_status))##
## prediction_rbfdot 0 1
## 0 0.63551764 0.32144242
## 1 0.02055060 0.02248934#Evaluate overall efficency
prop.table(table(prediction_rbfdot == test_data$loan_status))##
## FALSE TRUE
## 0.341993 0.658007a9 = accuracy(prediction_vanilladot, test_data$loan_status, "rbfdot SVM", TRUE)Data Prediction using kNN
knn_listing_pred <- knn(train = train_data, test = test_data,
cl = train_data$loan_status, k = 20)
# k = sqrt(population size: 33542) = 183
# confusion matrix
CrossTable(test_data$loan_status, knn_listing_pred,
dnn = c('Actual Loan Status', 'Predicted Loan Status'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 2579
##
##
## | Predicted Loan Status
## Actual Loan Status | 0 | 1 | Row Total |
## -------------------|-----------|-----------|-----------|
## 0 | 1595 | 97 | 1692 |
## | 9.350 | 64.392 | |
## | 0.943 | 0.057 | 0.656 |
## | 0.708 | 0.297 | |
## | 0.618 | 0.038 | |
## -------------------|-----------|-----------|-----------|
## 1 | 657 | 230 | 887 |
## | 17.836 | 122.831 | |
## | 0.741 | 0.259 | 0.344 |
## | 0.292 | 0.703 | |
## | 0.255 | 0.089 | |
## -------------------|-----------|-----------|-----------|
## Column Total | 2252 | 327 | 2579 |
## | 0.873 | 0.127 | |
## -------------------|-----------|-----------|-----------|
##
## Kappa(table(knn_listing_pred, test_data$loan_status))## value ASE z Pr(>|z|)
## Unweighted 0.2377 0.01799 13.21 7.87e-40
## Weighted 0.2377 0.01799 13.21 7.87e-40a1 = accuracy(knn_listing_pred, test_data$loan_status, "KNN with k = 20", TRUE)Decision Tree
default_decision_tree <- C5.0(train_data[-1], train_data$loan_status, trials = 3)
default_decision_tree##
## Call:
## C5.0.default(x = train_data[-1], y = train_data$loan_status, trials = 3)
##
## Classification Tree
## Number of samples: 10313
## Number of predictors: 64
##
## Number of boosting iterations: 3
## Average tree size: 10.3
##
## Non-standard options: attempt to group attributessummary(default_decision_tree)##
## Call:
## C5.0.default(x = train_data[-1], y = train_data$loan_status, trials = 3)
##
##
## C5.0 [Release 2.07 GPL Edition] Mon Dec 11 20:25:25 2017
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 10313 cases (65 attributes) from undefined.data
##
## ----- Trial 0: -----
##
## Decision tree:
##
## prosper_ratingAA > -0.2078642: 0 (422/34)
## prosper_ratingAA <= -0.2078642:
## :...prosper_ratingA > -0.4259548: 0 (1620/295)
## prosper_ratingA <= -0.4259548:
## :...listing_term <= -2.774434: 0 (55/7)
## listing_term > -2.774434:
## :...prosper_ratingB > -0.5059848: 0 (2084/545)
## prosper_ratingB <= -0.5059848:
## :...prosper_ratingC > -0.6173795: 0 (2807/990)
## prosper_ratingC <= -0.6173795:
## :...months2 > -0.1777783:
## :...day5 <= 0.0687658: 1 (93/27)
## : day5 > 0.0687658: 0 (40/15)
## months2 <= -0.1777783:
## :...amount_funded <= -1.034593: 0 (430/149)
## amount_funded > -1.034593:
## :...prosper_ratingHR <= -0.1921328:
## :...listing_term <= -0.7778947: 0 (1537/674)
## : listing_term > -0.7778947:
## : :...prosper_ratingD <= -0.4309001: 1 (341/147)
## : prosper_ratingD > -0.4309001: 0 (615/286)
## prosper_ratingHR > -0.1921328:
## :...months9 > -0.3231423: 0 (42/15)
## months9 <= -0.3231423:
## :...day.L > 0.7099153: 0 (36/12)
## day.L <= 0.7099153:
## :...months6 <= -0.2949719: 1 (177/58)
## months6 > -0.2949719: 0 (14/4)
##
## ----- Trial 1: -----
##
## Decision tree:
##
## prosper_ratingAA > -0.2078642: 0 (379.7/43.9)
## prosper_ratingAA <= -0.2078642:
## :...prosper_ratingA > -0.4259548: 0 (1527.7/381)
## prosper_ratingA <= -0.4259548:
## :...listing_term <= -2.774434: 0 (50.6/9)
## listing_term > -2.774434:
## :...prosper_ratingB > -0.5059848: 0 (2035.7/703.8)
## prosper_ratingB <= -0.5059848:
## :...prosper_ratingC <= -0.6173795:
## :...was_delinquent_derog <= 1.431238: 1 (3022.9/1401.1)
## : was_delinquent_derog > 1.431238: 0 (445.4/197.7)
## prosper_ratingC > -0.6173795:
## :...revolving_available_percent <= 1.166986: 0 (2539.9/1096.4)
## revolving_available_percent > 1.166986: 1 (311/129)
##
## ----- Trial 2: -----
##
## Decision tree:
##
## prosper_ratingB > -0.5059848: 0 (1159.1)
## prosper_ratingB <= -0.5059848:
## :...prosper_ratingA > -0.4259548: 0 (990.9)
## prosper_ratingA <= -0.4259548:
## :...prosper_ratingAA > -0.2078642: 0 (290.2)
## prosper_ratingAA <= -0.2078642:
## :...prosper_ratingC > -0.6173795: 0 (1689.8/157.3)
## prosper_ratingC <= -0.6173795:
## :...months2 > -0.1777783: 1 (123.2/49.4)
## months2 <= -0.1777783:
## :...listing_term <= -0.7778947: 0 (2875.2/897.7)
## listing_term > -0.7778947:
## :...prosper_ratingD <= -0.4309001: 1 (233/51.4)
## prosper_ratingD > -0.4309001: 0 (891.7/303.5)
##
##
## Evaluation on training data (10313 cases):
##
## Trial Decision Tree
## ----- ----------------
## Size Errors
##
## 0 15 3258(31.6%)
## 1 8 3564(34.6%)
## 2 8 3329(32.3%)
## boost 3257(31.6%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 6652 256 (a): class 0
## 3001 404 (b): class 1
##
##
## Attribute usage:
##
## 100.00% prosper_ratingA
## 100.00% prosper_ratingAA
## 100.00% prosper_ratingB
## 80.20% listing_term
## 59.89% prosper_ratingC
## 32.52% months2
## 32.24% was_delinquent_derog
## 30.95% amount_funded
## 27.22% revolving_available_percent
## 26.78% prosper_ratingHR
## 9.99% prosper_ratingD
## 2.61% months9
## 2.20% day.L
## 1.85% months6
## 1.29% day5
##
##
## Time: 0.7 secs##Evaluating model performance ----
default_decision_pred <- predict(default_decision_tree, test_data)
# cross tabulation of predicted versus actual classes
CrossTable(test_data$loan_status, default_decision_pred,
dnn = c('Actual Loan Status', 'Predicted Loan Status'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 2579
##
##
## | Predicted Loan Status
## Actual Loan Status | 0 | 1 | Row Total |
## -------------------|-----------|-----------|-----------|
## 0 | 1613 | 79 | 1692 |
## | 0.383 | 5.866 | |
## | 0.953 | 0.047 | 0.656 |
## | 0.666 | 0.500 | |
## | 0.625 | 0.031 | |
## -------------------|-----------|-----------|-----------|
## 1 | 808 | 79 | 887 |
## | 0.730 | 11.190 | |
## | 0.911 | 0.089 | 0.344 |
## | 0.334 | 0.500 | |
## | 0.313 | 0.031 | |
## -------------------|-----------|-----------|-----------|
## Column Total | 2421 | 158 | 2579 |
## | 0.939 | 0.061 | |
## -------------------|-----------|-----------|-----------|
##
## Kappa(table(default_decision_pred, test_data$loan_status))## value ASE z Pr(>|z|)
## Unweighted 0.05267 0.01344 3.918 8.916e-05
## Weighted 0.05267 0.01344 3.918 8.916e-05a2= accuracy(default_decision_pred, test_data$loan_status, "C5.0 Decision Tree", TRUE)Improving Decision Trees: Boosting and Cost Matrix
Lets attempt to improve your decision tree model by adding Adaptive Boosting for 10 trials.
# creating a decision tree prediction model using 10 trials
Decision_Tree_Model_Bagged <- C5.0(train_data[-1], train_data$loan_status, trials = 10)
# create a factor vector of predictions on test data
Decision_Tree_Predict_Bagged <- predict(Decision_Tree_Model_Bagged, test_data)
# creating a proportion table to check model accuacy
prop.table(table(Decision_Tree_Predict_Bagged == train_data$loan_status))## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length## Warning in `==.default`(Decision_Tree_Predict_Bagged, train_data
## $loan_status): longer object length is not a multiple of shorter object
## length##
## FALSE TRUE
## 0.3614855 0.6385145# cross tabulation of predicted v/s actual classes
CrossTable(test_data$loan_status, Decision_Tree_Predict_Bagged,
dnn = c('Actual Loan Status', 'Predicted Loan Status'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 2579
##
##
## | Predicted Loan Status
## Actual Loan Status | 0 | 1 | Row Total |
## -------------------|-----------|-----------|-----------|
## 0 | 1567 | 125 | 1692 |
## | 1.211 | 10.985 | |
## | 0.926 | 0.074 | 0.656 |
## | 0.675 | 0.488 | |
## | 0.608 | 0.048 | |
## -------------------|-----------|-----------|-----------|
## 1 | 756 | 131 | 887 |
## | 2.309 | 20.955 | |
## | 0.852 | 0.148 | 0.344 |
## | 0.325 | 0.512 | |
## | 0.293 | 0.051 | |
## -------------------|-----------|-----------|-----------|
## Column Total | 2323 | 256 | 2579 |
## | 0.901 | 0.099 | |
## -------------------|-----------|-----------|-----------|
##
## Kappa(table(Decision_Tree_Predict_Bagged, test_data$loan_status))## value ASE z Pr(>|z|)
## Unweighted 0.08885 0.01615 5.501 3.783e-08
## Weighted 0.08885 0.01615 5.501 3.783e-08a3= accuracy(default_decision_pred, test_data$loan_status, "C5.0 Decision Tree with 10 trials", TRUE)Lets attempt to improve your model with a cost matrix.
# cost matrix
Decision_Tree_Error_Cost_Matrix <- matrix(c(0, 1, 4, 0), nrow = 2)
# create model - apply the cost matrix to the tree
Decision_Tree_Error_Cost_Model <- C5.0(train_data[-1],
train_data$loan_status,
costs = Decision_Tree_Error_Cost_Matrix)## Warning in C5.0.default(train_data[-1], train_data$loan_status, costs = Decision_Tree_Error_Cost_Matrix):
## no dimnames were given for the cost matrix; the factor levels will be used# predict - using model and test data set
Decision_Tree_Error_Cost_Prediction <- predict(Decision_Tree_Error_Cost_Model, test_data)
# creating a proportion table to check model accuacy
prop.table(table(Decision_Tree_Error_Cost_Prediction == train_data$loan_status))## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length## Warning in `==.default`(Decision_Tree_Error_Cost_Prediction, train_data
## $loan_status): longer object length is not a multiple of shorter object
## length##
## FALSE TRUE
## 0.5980801 0.4019199# cross tabulation of predicted v/s actual classes
CrossTable(test_data$loan_status, Decision_Tree_Error_Cost_Prediction,
dnn = c('actual loan_status', 'predicted loan_status'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 2579
##
##
## | predicted loan_status
## actual loan_status | 0 | 1 | Row Total |
## -------------------|-----------|-----------|-----------|
## 0 | 441 | 1251 | 1692 |
## | 21.229 | 5.622 | |
## | 0.261 | 0.739 | 0.656 |
## | 0.817 | 0.614 | |
## | 0.171 | 0.485 | |
## -------------------|-----------|-----------|-----------|
## 1 | 99 | 788 | 887 |
## | 40.495 | 10.725 | |
## | 0.112 | 0.888 | 0.344 |
## | 0.183 | 0.386 | |
## | 0.038 | 0.306 | |
## -------------------|-----------|-----------|-----------|
## Column Total | 540 | 2039 | 2579 |
## | 0.209 | 0.791 | |
## -------------------|-----------|-----------|-----------|
##
## a4= accuracy(Decision_Tree_Error_Cost_Prediction, test_data$loan_status, "C5.0 Decision Tree with cost matrix", TRUE)Cross Validation
run a 10-fold cross validation in an attempt to improve the model -
# randomizing the data set
set.seed(12345)
folds <- createFolds(train_data$loan_status, k = 10)
folding_results <- lapply(folds, function(x) {
fold_train <- train_data[x, ]
fold_test <- test_data[-x, ]
fold_model <- C5.0(loan_status ~ ., data = fold_train)
prediction <- predict(fold_model, fold_test)
actual <- fold_test$loan_status
kappa <- kappa2(data.frame(actual, prediction))$value
return(kappa)
})
str(folding_results)## List of 10
## $ Fold01: num 0.0679
## $ Fold02: num 0.0326
## $ Fold03: num 0.11
## $ Fold04: num 0.0782
## $ Fold05: num 0.066
## $ Fold06: num 0.0977
## $ Fold07: num 0.0671
## $ Fold08: num 0.0363
## $ Fold09: num 0.0703
## $ Fold10: num 0.0965mean(unlist(folding_results))## [1] 0.07225935Random Forest:
rf <- randomForest(loan_status ~ listing_term+stated_monthly_income+income_verifiableTRUE+employment_status_descriptionother+months_employed+monthly_debt+public_records_last10_years+inquiries_last6_months+total_open_revolving_accounts+installment_balance+revolving_available_percent+total_inquiries+scorex, data = train_data)
rf_pred <- predict(rf, test_data)
a7 = accuracy(rf_pred, test_data$loan_status, "Random Forest", TRUE)
Kappa(table(rf_pred, test_data$loan_status))## value ASE z Pr(>|z|)
## Unweighted 0.04918 0.01473 3.338 0.0008451
## Weighted 0.04918 0.01473 3.338 0.0008451Stacked Model
acc_predictions = c(a1,a2,a3,a4,a7,a8,a9 )
#acc_predictions = c(a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)
names = c("KNN", "Decision Tree Regression","Decision Tree Regression with 10 trials", "Decision Tree Regression with Cost Matrix", "Random Forest","SVM with Vanilla Kernal","SVM Radial Basis Kernal")
#names = c("Knn","Decision C5.0 Tree Regression","Decision Tree C5.0 Regression trails 10", "Decision Tree C5.0 with Cost Model", "Random Forest", "Stacked Model")
acc_mat <- data.frame(ModelName = names, accuracy = acc_predictions) %>% print## ModelName accuracy
## 1 KNN 0.7076386
## 2 Decision Tree Regression 0.6560682
## 3 Decision Tree Regression with 10 trials 0.6560682
## 4 Decision Tree Regression with Cost Matrix 0.4765413
## 5 Random Forest 0.6487010
## 6 SVM with Vanilla Kernal 0.6626599
## 7 SVM Radial Basis Kernal 0.6626599dotchart(acc_predictions, labels = names, main = "Accuracy of the models", xlab = "Accuracy")
Data Prediction using Logistic Regression
logReg <- glm(loan_status ~ ., data = listingsample, family = "binomial")## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(logReg)##
## Call:
## glm(formula = loan_status ~ ., family = "binomial", data = listingsample)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0592 -0.5818 -0.4608 -0.3332 3.7997
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 7.690e-01 5.063e-01 1.519
## amount_funded 3.325e-05 3.668e-06 9.065
## prosper_ratingAA -8.063e-01 1.674e-01 -4.816
## prosper_ratingB 3.086e-01 6.867e-02 4.494
## prosper_ratingC 7.027e-01 6.717e-02 10.461
## prosper_ratingD 1.013e+00 7.491e-02 13.526
## prosper_ratingE 1.275e+00 8.140e-02 15.660
## prosper_ratingHR 1.555e+00 1.065e-01 14.606
## listing_term 7.971e-03 1.500e-03 5.314
## scorex -4.365e-03 6.046e-04 -7.220
## stated_monthly_income -7.784e-05 7.966e-06 -9.771
## income_verifiableTRUE -3.451e-01 1.196e-01 -2.886
## dti_wprosper_loan 1.562e-03 2.377e-03 0.657
## employment_status_descriptionFulltime -1.066e-02 1.614e-01 -0.066
## employment_status_descriptionNotEmployed -3.123e-01 3.295e-01 -0.948
## employment_status_descriptionother 1.876e-01 7.120e-02 2.635
## employment_status_descriptionParttime 1.602e-01 1.164e+00 0.138
## employment_status_descriptionRetired 1.142e-01 6.394e-01 0.179
## employment_status_descriptionSelfEmployed -1.205e-01 1.359e-01 -0.886
## months_employed -5.747e-04 1.815e-04 -3.166
## lender_indicator -1.760e-01 8.808e-02 -1.998
## monthly_debt 1.073e-04 3.438e-05 3.121
## current_delinquencies -1.871e-01 3.517e-01 -0.532
## delinquencies_last7_years -6.001e-03 9.504e-03 -0.631
## public_records_last10_years 2.226e-02 2.541e-02 0.876
## public_records_last12_months 4.337e-02 9.378e-02 0.462
## credit_lines_last7_years 1.686e-01 3.502e-01 0.482
## inquiries_last6_months 3.727e-02 1.595e-02 2.337
## amount_delinquent 9.913e-07 2.189e-06 0.453
## current_credit_lines -2.644e-03 1.348e-02 -0.196
## open_credit_lines -4.643e-03 1.536e-02 -0.302
## bankcard_utilization -1.949e-01 1.075e-01 -1.813
## total_open_revolving_accounts 1.376e-02 8.619e-03 1.596
## installment_balance 8.477e-07 5.485e-07 1.546
## real_estate_balance 4.733e-08 2.094e-07 0.226
## revolving_balance -4.531e-07 8.522e-07 -0.532
## real_estate_payment -7.548e-06 1.761e-05 -0.429
## revolving_available_percent 2.793e-03 1.334e-03 2.095
## total_inquiries 1.110e-02 5.488e-03 2.023
## total_trade_items NA NA NA
## satisfactory_accounts -1.719e-01 3.502e-01 -0.491
## now_delinquent_derog NA NA NA
## was_delinquent_derog -1.969e-01 3.501e-01 -0.562
## is_homeownerTRUE 8.810e-02 4.817e-02 1.829
## quarters -1.618e-01 1.777e-01 -0.911
## months10 2.608e-01 5.362e-01 0.486
## months11 2.087e-01 5.450e-01 0.383
## months12 6.640e-02 5.453e-01 0.122
## months2 1.415e-01 1.349e-01 1.049
## months3 5.080e-02 1.254e-01 0.405
## months4 3.096e-02 2.159e-01 0.143
## months5 1.378e-01 2.132e-01 0.646
## months6 8.523e-02 2.128e-01 0.400
## months7 1.857e-01 3.700e-01 0.502
## months8 2.158e-01 3.739e-01 0.577
## months9 7.088e-02 3.739e-01 0.190
## day.L -8.834e-03 3.945e-02 -0.224
## day.Q -4.533e-02 3.822e-02 -1.186
## day.C -1.252e-02 3.767e-02 -0.332
## day^4 -4.601e-03 3.614e-02 -0.127
## is_major_gdpNot Major 5.866e-02 3.504e-02 1.674
## delinq_agg 1.023e-03 2.113e-03 0.484
## Pr(>|z|)
## (Intercept) 0.12878
## amount_funded < 2e-16 ***
## prosper_ratingAA 1.46e-06 ***
## prosper_ratingB 6.98e-06 ***
## prosper_ratingC < 2e-16 ***
## prosper_ratingD < 2e-16 ***
## prosper_ratingE < 2e-16 ***
## prosper_ratingHR < 2e-16 ***
## listing_term 1.07e-07 ***
## scorex 5.20e-13 ***
## stated_monthly_income < 2e-16 ***
## income_verifiableTRUE 0.00391 **
## dti_wprosper_loan 0.51124
## employment_status_descriptionFulltime 0.94737
## employment_status_descriptionNotEmployed 0.34315
## employment_status_descriptionother 0.00843 **
## employment_status_descriptionParttime 0.89059
## employment_status_descriptionRetired 0.85828
## employment_status_descriptionSelfEmployed 0.37535
## months_employed 0.00155 **
## lender_indicator 0.04569 *
## monthly_debt 0.00181 **
## current_delinquencies 0.59468
## delinquencies_last7_years 0.52774
## public_records_last10_years 0.38110
## public_records_last12_months 0.64377
## credit_lines_last7_years 0.63006
## inquiries_last6_months 0.01943 *
## amount_delinquent 0.65068
## current_credit_lines 0.84449
## open_credit_lines 0.76251
## bankcard_utilization 0.06983 .
## total_open_revolving_accounts 0.11050
## installment_balance 0.12218
## real_estate_balance 0.82118
## revolving_balance 0.59494
## real_estate_payment 0.66812
## revolving_available_percent 0.03621 *
## total_inquiries 0.04307 *
## total_trade_items NA
## satisfactory_accounts 0.62355
## now_delinquent_derog NA
## was_delinquent_derog 0.57378
## is_homeownerTRUE 0.06744 .
## quarters 0.36248
## months10 0.62669
## months11 0.70171
## months12 0.90309
## months2 0.29424
## months3 0.68540
## months4 0.88598
## months5 0.51806
## months6 0.68885
## months7 0.61577
## months8 0.56391
## months9 0.84965
## day.L 0.82284
## day.Q 0.23550
## day.C 0.73955
## day^4 0.89869
## is_major_gdpNot Major 0.09417 .
## delinq_agg 0.62819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25592 on 33299 degrees of freedom
## Residual deviance: 24157 on 33240 degrees of freedom
## AIC: 24277
##
## Number of Fisher Scoring iterations: 6Comments -
the following variables are stastically significant and have a fairly strong impact on on our response variable, loan_status -
1. amount_funded
2. listing_term
3. scorex
4. stated_monthly_income
5. income_verifiableTRUE
6. employment_status_descriptionother
7. months_employed
8. monthly_debt
9. inquiries_last6_months
10. total_open_revolving_accounts
11. installment_balance
Most of these are summary variables and their presence masks the impact other variables can have on loan_status.
As a result, we will run another regression without these summary variables
logReg_revised <- glm(loan_status ~ . ,
data = listing_no_agg, family = "binomial")## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(logReg_revised)##
## Call:
## glm(formula = loan_status ~ ., family = "binomial", data = listing_no_agg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.8409 -0.5675 -0.4891 -0.3921 4.2618
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -1.108e+00 2.707e-01 -4.093
## amount_funded 3.700e-06 3.362e-06 1.101
## listing_term 1.104e-02 1.450e-03 7.611
## stated_monthly_income -1.054e-04 8.123e-06 -12.981
## income_verifiableTRUE -3.774e-01 1.186e-01 -3.182
## employment_status_descriptionFulltime -9.763e-02 1.578e-01 -0.619
## employment_status_descriptionNotEmployed 1.649e-01 3.238e-01 0.509
## employment_status_descriptionother 1.752e-01 7.025e-02 2.494
## employment_status_descriptionParttime 6.089e-02 1.106e+00 0.055
## employment_status_descriptionRetired -1.274e-01 6.345e-01 -0.201
## employment_status_descriptionSelfEmployed -1.851e-01 1.350e-01 -1.371
## months_employed -7.309e-04 1.803e-04 -4.055
## lender_indicator -4.337e-01 8.448e-02 -5.134
## monthly_debt 2.177e-04 3.955e-05 5.504
## current_delinquencies -2.742e-01 3.537e-01 -0.775
## delinquencies_last7_years -1.340e-02 9.400e-03 -1.426
## public_records_last10_years 5.210e-02 2.433e-02 2.141
## public_records_last12_months 4.459e-02 9.140e-02 0.488
## credit_lines_last7_years 3.048e-01 3.525e-01 0.865
## inquiries_last6_months 1.184e-01 1.517e-02 7.807
## amount_delinquent 2.987e-06 2.193e-06 1.362
## current_credit_lines 2.546e-03 1.343e-02 0.190
## open_credit_lines -1.310e-02 1.528e-02 -0.857
## bankcard_utilization 1.204e-01 1.082e-01 1.112
## total_open_revolving_accounts 3.180e-02 8.575e-03 3.709
## installment_balance 1.471e-06 5.525e-07 2.662
## real_estate_balance -3.895e-07 2.354e-07 -1.655
## revolving_balance -2.215e-06 9.381e-07 -2.361
## real_estate_payment -1.052e-05 2.464e-05 -0.427
## revolving_available_percent -5.333e-03 1.271e-03 -4.196
## total_inquiries 3.029e-02 5.236e-03 5.785
## total_trade_items NA NA NA
## satisfactory_accounts -3.156e-01 3.525e-01 -0.895
## now_delinquent_derog NA NA NA
## was_delinquent_derog -3.100e-01 3.524e-01 -0.880
## is_homeownerTRUE -5.464e-02 4.795e-02 -1.139
## quarters -1.825e-01 1.758e-01 -1.038
## months10 2.785e-01 5.303e-01 0.525
## months11 1.559e-01 5.392e-01 0.289
## months12 1.052e-02 5.395e-01 0.020
## months2 1.577e-01 1.335e-01 1.181
## months3 -1.915e-02 1.242e-01 -0.154
## months4 -1.137e-02 2.137e-01 -0.053
## months5 3.503e-02 2.109e-01 0.166
## months6 -6.265e-03 2.106e-01 -0.030
## months7 9.866e-02 3.661e-01 0.270
## months8 8.167e-02 3.699e-01 0.221
## months9 5.868e-02 3.699e-01 0.159
## day.L -9.141e-03 3.909e-02 -0.234
## day.Q -4.651e-02 3.785e-02 -1.229
## day.C -1.714e-02 3.729e-02 -0.460
## day^4 -3.568e-03 3.577e-02 -0.100
## is_major_gdpNot Major 6.242e-02 3.471e-02 1.798
## delinq_agg 2.765e-03 2.084e-03 1.327
## Pr(>|z|)
## (Intercept) 4.25e-05 ***
## amount_funded 0.271000
## listing_term 2.71e-14 ***
## stated_monthly_income < 2e-16 ***
## income_verifiableTRUE 0.001462 **
## employment_status_descriptionFulltime 0.536116
## employment_status_descriptionNotEmployed 0.610424
## employment_status_descriptionother 0.012644 *
## employment_status_descriptionParttime 0.956109
## employment_status_descriptionRetired 0.840898
## employment_status_descriptionSelfEmployed 0.170359
## months_employed 5.02e-05 ***
## lender_indicator 2.83e-07 ***
## monthly_debt 3.71e-08 ***
## current_delinquencies 0.438178
## delinquencies_last7_years 0.153877
## public_records_last10_years 0.032244 *
## public_records_last12_months 0.625625
## credit_lines_last7_years 0.387213
## inquiries_last6_months 5.85e-15 ***
## amount_delinquent 0.173250
## current_credit_lines 0.849590
## open_credit_lines 0.391210
## bankcard_utilization 0.266104
## total_open_revolving_accounts 0.000208 ***
## installment_balance 0.007778 **
## real_estate_balance 0.097942 .
## revolving_balance 0.018206 *
## real_estate_payment 0.669449
## revolving_available_percent 2.72e-05 ***
## total_inquiries 7.26e-09 ***
## total_trade_items NA
## satisfactory_accounts 0.370560
## now_delinquent_derog NA
## was_delinquent_derog 0.379119
## is_homeownerTRUE 0.254530
## quarters 0.299323
## months10 0.599549
## months11 0.772519
## months12 0.984436
## months2 0.237453
## months3 0.877411
## months4 0.957566
## months5 0.868084
## months6 0.976267
## months7 0.787535
## months8 0.825252
## months9 0.873944
## day.L 0.815113
## day.Q 0.219191
## day.C 0.645690
## day^4 0.920544
## is_major_gdpNot Major 0.072159 .
## delinq_agg 0.184532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25592 on 33299 degrees of freedom
## Residual deviance: 24791 on 33248 degrees of freedom
## AIC: 24895
##
## Number of Fisher Scoring iterations: 5Comments -
After removing the summary variable, we know that the following variables impact loan_status
1. amount_funded
2. listing_term
3. scorex
4. stated_monthly_income
5. income_verifiableTRUE
6. employment_status_descriptionother
7. months_employed
8. monthly_debt
9. inquiries_last6_months
10. total_open_revolving_accounts
11. installment_balance
Most of these are summary variables and their presence masks the impact other variables can have on loan_status.
As a result, we will run another regression without these summary variables
Getting Data Ready for Machine Learning Models
Since our dataset is relatively small ~33,300 after cleaning and the proportion of our predictor variable is low ~13%, we refine our dataset to have a more equal distribution of the two outcomes. This will help prevent overfitting or poor classification results. From our datset, about 4292 default and 29008 do not default.
default_listings <- listingsample[listingsample$loan_status == 1, ]
no_default_listings <- listingsample[listingsample$loan_status == 0, ]
index <- sample(1:nrow(no_default_listings), 8600)
no_default_listings_sample <- no_default_listings[index, ]
listingsample <- rbind(no_default_listings_sample, default_listings)# Randomizing dataset
set.seed(12345)
listing_rand <-listingsample
listing_rand$loan_status <- as.factor(listing_rand$loan_status)
loan_status <- listing_rand[1]
str(listing_rand)## 'data.frame': 12892 obs. of 39 variables:
## $ loan_status : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ amount_funded : num 19000 4000 13500 5000 15000 ...
## $ prosper_rating : Factor w/ 7 levels "A","AA","B","C",..: 4 6 3 5 2 1 5 5 3 2 ...
## $ listing_term : int 60 36 60 36 60 36 36 60 36 60 ...
## $ scorex : num 630 645 778 610 778 ...
## $ stated_monthly_income : num 5417 2917 4667 5667 5833 ...
## $ income_verifiable : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ...
## $ dti_wprosper_loan : num 0.25 0.28 0.34 0.15 0.26 0.23 0.07 0.31 0.44 0.22 ...
## $ employment_status_description: Factor w/ 7 levels "Employed","Fulltime",..: 1 1 1 1 1 1 4 1 1 4 ...
## $ months_employed : num 244 126 294 249 93 143 85 93 20 192 ...
## $ lender_indicator : int 0 0 0 0 0 0 0 0 0 0 ...
## $ monthly_debt : int 864 642 1225 627 1195 1206 131 673 1426 629 ...
## $ current_delinquencies : int 0 0 0 1 0 0 0 0 0 0 ...
## $ delinquencies_last7_years : int 2 9 0 9 0 0 31 0 1 0 ...
## $ public_records_last10_years : int 1 0 0 1 0 0 1 0 1 0 ...
## $ public_records_last12_months : int 0 0 0 0 0 0 0 0 0 0 ...
## $ credit_lines_last7_years : int 32 14 30 28 20 33 46 16 33 11 ...
## $ inquiries_last6_months : int 1 2 1 3 0 0 1 0 0 0 ...
## $ amount_delinquent : int 0 0 0 0 0 0 0 0 0 0 ...
## $ current_credit_lines : int 11 5 16 8 15 13 4 9 18 7 ...
## $ open_credit_lines : int 11 5 13 7 14 12 4 7 17 7 ...
## $ bankcard_utilization : num 0.9 0 0.23 0.94 0.54 0.27 0.97 0.78 0.37 0.03 ...
## $ total_open_revolving_accounts: int 12 2 9 9 10 8 3 7 9 6 ...
## $ installment_balance : int 18857 23507 18005 21474 14383 25131 0 0 40289 17382 ...
## $ real_estate_balance : int 0 57113 229023 0 453366 173057 382344 0 198503 0 ...
## $ revolving_balance : int 15540 552 6288 4890 26314 18887 2937 13243 11313 685 ...
## $ real_estate_payment : int 0 674 1596 0 3486 1239 2997 0 1385 0 ...
## $ revolving_available_percent : int 49 82 77 36 54 73 2 35 76 97 ...
## $ total_inquiries : int 5 3 4 13 2 1 2 2 1 1 ...
## $ total_trade_items : int 32 14 30 28 20 33 46 16 33 11 ...
## $ satisfactory_accounts : int 25 11 30 26 20 32 31 16 31 11 ...
## $ now_delinquent_derog : int 0 0 0 1 0 0 0 0 0 0 ...
## $ was_delinquent_derog : int 7 3 0 1 0 1 15 0 2 0 ...
## $ is_homeowner : Factor w/ 2 levels "FALSE","TRUE": 1 2 2 1 2 2 2 1 2 1 ...
## $ quarters : int 4 3 2 4 2 1 4 4 4 3 ...
## $ months : Factor w/ 12 levels "1","10","11",..: 2 10 7 4 7 6 3 3 4 12 ...
## $ day : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 3 4 4 6 3 2 6 6 3 5 ...
## $ is_major_gdp : Factor w/ 2 levels "Major","Not Major": 2 2 2 2 2 1 2 2 2 1 ...
## $ delinq_agg : num 17 83 0 41 0 4 159 0 9 0 ...l_dummy <-model.matrix(~ . -1, data = listing_rand[-1])
l_dummy <- as.data.frame(scale(l_dummy))
l_dummy<- cbind(loan_status, l_dummy)
l_dummy## loan_status amount_funded prosper_ratingA prosper_ratingAA
## 22546 0 1.317651977 -0.4279863 -0.2050052
## 29151 0 -0.983038056 -0.4279863 -0.2050052
## 18166 0 0.474065631 -0.4279863 -0.2050052
## 20108 0 -0.829658721 -0.4279863 -0.2050052
## 21654 0 0.704134635 -0.4279863 4.8775467
## 10433 0 0.159637993 2.3363421 -0.2050052
## 11330 0 -0.369520714 -0.4279863 -0.2050052
## 10690 0 -0.062762043 -0.4279863 -0.2050052
## 19147 0 0.243996628 -0.4279863 -0.2050052
## 18225 0 -0.062762043 -0.4279863 4.8775467
## 30110 0 -1.289796727 2.3363421 -0.2050052
## 27870 0 -0.983038056 -0.4279863 -0.2050052
## 27498 0 0.243996628 -0.4279863 -0.2050052
## 19997 0 -0.676279385 -0.4279863 -0.2050052
## 17368 0 -0.446210382 -0.4279863 -0.2050052
## 20599 0 -0.983038056 -0.4279863 -0.2050052
## 14673 0 -0.983038056 -0.4279863 -0.2050052
## 24600 0 0.704134635 -0.4279863 -0.2050052
## 8447 0 -1.289796727 -0.4279863 -0.2050052
## 27908 0 -0.062762043 -0.4279863 -0.2050052
## 12890 0 0.704134635 2.3363421 -0.2050052
## 28529 0 0.704134635 2.3363421 -0.2050052
## 2533 0 1.234265767 -0.4279863 -0.2050052
## 24376 0 3.771721346 -0.4279863 -0.2050052
## 6241 0 1.471031312 2.3363421 -0.2050052
## 564 0 -0.062762043 -0.4279863 -0.2050052
## 20961 0 -0.983038056 -0.4279863 -0.2050052
## 21541 0 -0.983038056 -0.4279863 -0.2050052
## 13045 0 1.471031312 2.3363421 -0.2050052
## 13215 0 -0.062762043 -0.4279863 4.8775467
## 13 0 0.704134635 -0.4279863 -0.2050052
## 9141 0 -0.062762043 -0.4279863 -0.2050052
## 2979 0 0.704134635 -0.4279863 -0.2050052
## 4648 0 1.471031312 -0.4279863 -0.2050052
## 4671 0 2.237927990 -0.4279863 -0.2050052
## 31959 0 -1.136417392 -0.4279863 -0.2050052
## 7316 0 1.624410648 -0.4279863 -0.2050052
## 13792 0 -0.983038056 -0.4279863 -0.2050052
## 20140 0 -0.062762043 2.3363421 -0.2050052
## 1565 0 -0.983038056 -0.4279863 -0.2050052
## 26701 0 0.704134635 -0.4279863 -0.2050052
## 16556 0 -0.983038056 -0.4279863 -0.2050052
## 18104 0 1.164272641 2.3363421 -0.2050052
## 11927 0 0.780824302 2.3363421 -0.2050052
## 9144 0 -0.062762043 -0.4279863 -0.2050052
## 26961 0 1.471031312 -0.4279863 4.8775467
## 32960 0 0.704134635 -0.4279863 -0.2050052
## 1527 0 -1.136417392 -0.4279863 -0.2050052
## 14725 0 -0.829658721 -0.4279863 -0.2050052
## 29171 0 -0.752969053 2.3363421 -0.2050052
## 1634 0 0.704134635 -0.4279863 -0.2050052
## 12257 0 -0.599589717 -0.4279863 -0.2050052
## 32905 0 -0.522900050 -0.4279863 -0.2050052
## 15486 0 -0.674745592 -0.4279863 -0.2050052
## 26180 0 -0.983038056 -0.4279863 -0.2050052
## 1571 0 -0.216141379 2.3363421 -0.2050052
## 8986 0 2.237927990 -0.4279863 -0.2050052
## 22031 0 0.704134635 -0.4279863 -0.2050052
## 20038 0 -0.032322380 -0.4279863 -0.2050052
## 26910 0 -1.136417392 -0.4279863 -0.2050052
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## 30062 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 21136 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 17327 1.9766628 -0.6176192 -0.4355723 -0.3874733
## 28999 -0.5058639 1.6189951 -0.4355723 -0.3874733
## 24453 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 5817 -0.5058639 -0.6176192 2.2956522 -0.3874733
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## 17592 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 25447 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 8164 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 22919 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 1134 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 6568 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 21967 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 25480 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 14288 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 30877 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 11490 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 26415 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 19773 1.9766628 -0.6176192 -0.4355723 -0.3874733
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## 27774 -0.5058639 -0.6176192 -0.4355723 2.5806228
## 29012 -0.5058639 -0.6176192 -0.4355723 -0.3874733
## 16394 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 18197 -0.5058639 1.6189951 -0.4355723 -0.3874733
## 9251 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 5012 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 16865 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 23683 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 4407 -0.5058639 -0.6176192 2.2956522 -0.3874733
## 8697 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 29252 1.9766628 -0.6176192 -0.4355723 -0.3874733
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## 33083 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 44 -0.5058639 -0.6176192 -0.4355723 -0.3874733
## 20528 -0.5058639 -0.6176192 -0.4355723 2.5806228
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## 29119 -0.5058639 1.6189951 -0.4355723 -0.3874733
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## 33211 -0.5058639 -0.6176192 -0.4355723 -0.3874733
## 4672 -0.5058639 -0.6176192 2.2956522 -0.3874733
## 28324 1.9766628 -0.6176192 -0.4355723 -0.3874733
## 28701 -0.5058639 -0.6176192 -0.4355723 -0.3874733
## 14051 1.9766628 -0.6176192 -0.4355723 -0.3874733
## 26578 -0.5058639 -0.6176192 -0.4355723 2.5806228
## prosper_ratingHR listing_term scorex stated_monthly_income
## 22546 -0.190171 1.205731 -1.6093546 -1.398651e-01
## 29151 -0.190171 -0.782029 -1.2881396 -7.603992e-01
## 18166 -0.190171 1.205731 1.5599664 -3.260253e-01
## 20108 -0.190171 -0.782029 -2.0376412 -7.781171e-02
## 21654 -0.190171 1.205731 1.5599664 -3.644278e-02
## 10433 -0.190171 -0.782029 1.2173371 2.308420e-01
## 11330 -0.190171 -0.782029 0.6498573 1.576189e-01
## 10690 -0.190171 1.205731 0.1252062 -7.397148e-01
## 19147 -0.190171 -0.782029 1.5599664 -4.501322e-01
## 18225 -0.190171 1.205731 1.5599664 -5.535545e-01
## 30110 -0.190171 -0.782029 -0.6457097 -4.077083e-01
## 27870 -0.190171 1.205731 -0.6457097 -1.075506e+00
## 27498 -0.190171 1.205731 0.6498573 -3.880788e-01
## 19997 -0.190171 -0.782029 -0.6457097 -5.535545e-01
## 17368 -0.190171 -0.782029 -0.2174231 -6.569769e-01
## 20599 -0.190171 1.205731 -0.6457097 -4.915011e-01
## 14673 -0.190171 -0.782029 -0.2174231 3.772466e-01
## 24600 -0.190171 1.205731 0.1252062 -2.432875e-01
## 8447 -0.190171 -0.782029 0.1252062 -9.680713e-01
## 27908 -0.190171 1.205731 1.2173371 -4.294477e-01
## 12890 -0.190171 1.205731 1.2173371 1.704019e-01
## 28529 -0.190171 -0.782029 -0.2174231 -7.781171e-02
## 2533 -0.190171 -0.782029 -0.6457097 4.307296e+00
## 24376 -0.190171 1.205731 1.2173371 8.736739e-01
## 6241 -0.190171 -0.782029 1.2173371 5.634069e-01
## 564 -0.190171 1.205731 0.1252062 -6.884173e-01
## 20961 -0.190171 1.205731 -2.0376412 6.461447e-01
## 21541 -0.190171 -0.782029 -0.6457097 -7.781171e-02
## 13045 -0.190171 1.205731 1.2173371 6.697958e-02
## 13215 -0.190171 -0.782029 1.2173371 5.863046e-01
## 13 -0.190171 1.205731 0.1252062 1.526841e-02
## 9141 -0.190171 1.205731 -0.6457097 1.704019e-01
## 2979 -0.190171 1.205731 -0.2174231 -3.467098e-01
## 4648 -0.190171 1.205731 0.6498573 4.926166e-03
## 4671 -0.190171 1.205731 0.1252062 5.134675e+00
## 31959 -0.190171 -2.769789 0.6498573 -7.086881e-01
## 7316 -0.190171 1.205731 -0.6457097 1.290330e-01
## 13792 -0.190171 -0.782029 -1.2881396 -2.432875e-01
## 20140 -0.190171 -0.782029 1.2173371 -8.431371e-01
## 1565 -0.190171 -0.782029 1.2173371 1.189112e+00
## 26701 -0.190171 1.205731 0.6498573 1.704019e-01
## 16556 -0.190171 -0.782029 -0.2174231 -9.672439e-01
## 18104 -0.190171 1.205731 1.5599664 1.704019e-01
## 11927 -0.190171 -0.782029 1.2173371 -3.644278e-02
## 9144 -0.190171 1.205731 1.2173371 6.875137e-01
## 26961 -0.190171 -0.782029 1.2173371 2.741966e-01
## 32960 -0.190171 1.205731 0.6498573 -3.260253e-01
## 1527 -0.190171 -0.782029 -1.6093546 -5.742390e-01
## 14725 -0.190171 1.205731 0.1252062 -3.644278e-02
## 29171 -0.190171 -0.782029 0.6498573 9.977807e-01
## 1634 -0.190171 1.205731 0.1252062 8.943584e-01
## 12257 -0.190171 -0.782029 0.6498573 -2.432875e-01
## 32905 -0.190171 -0.782029 0.1252062 7.909360e-01
## 15486 -0.190171 1.205731 1.2173371 2.738243e-01
## 26180 -0.190171 1.205731 -0.9669246 -7.810837e-01
## 1571 -0.190171 1.205731 0.6498573 1.869288e-01
## 8986 -0.190171 -0.782029 -1.6093546 9.977807e-01
## 22031 -0.190171 -0.782029 0.1252062 -2.639719e-01
## 20038 -0.190171 -0.782029 0.1252062 -1.398651e-01
## 26910 5.258017 -0.782029 -1.2881396 -8.390002e-01
## 31831 -0.190171 1.205731 -1.6093546 8.943584e-01
## 15755 -0.190171 1.205731 -1.6093546 -8.834511e-01
## 1109 -0.190171 1.205731 0.6498573 -1.398651e-01
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## 32207 -0.6932043 -1.215766e-01 -0.63306200
## 3352 0.9006588 -1.215766e-01 -0.04046559
## 14732 -0.6932043 -1.215766e-01 -0.63306200
## 5588 0.9006588 -1.215766e-01 1.34225936
## 24839 -0.6932043 -1.215766e-01 0.55213082
## 16476 0.1037272 -1.215766e-01 0.55213082
## 11490 -0.6932043 -1.215766e-01 -0.83059413
## 6351 -0.6932043 2.493253e-01 -0.43552986
## 29684 0.9006588 -1.215766e-01 -0.04046559
## 23753 0.9006588 -1.215766e-01 1.34225936
## 21179 -0.6932043 -1.215766e-01 1.73732364
## 26274 -0.6932043 -1.215766e-01 -0.63306200
## 23149 0.1037272 -1.215766e-01 0.35459868
## 32334 -0.6932043 -1.215766e-01 -1.02812627
## 20081 -0.6932043 -1.215766e-01 1.14472723
## 11088 0.1037272 -1.215766e-01 -0.04046559
## 18392 0.1037272 -1.215766e-01 -0.83059413
## 25678 0.9006588 -1.215766e-01 0.15706655
## 23968 -0.6932043 -1.215766e-01 1.73732364
## 26981 0.9006588 -1.215766e-01 1.14472723
## 6512 0.1037272 -6.819869e-02 0.94719509
## 20100 -0.6932043 -1.215766e-01 -1.22565841
## 14325 0.1037272 -1.215766e-01 1.53979150
## 5710 0.1037272 1.076808e+00 -0.43552986
## 9197 -0.6932043 -1.215766e-01 -0.04046559
## 31441 0.1037272 -1.215766e-01 0.35459868
## 27775 -0.6932043 -1.215766e-01 1.73732364
## 16653 0.1037272 -1.215766e-01 1.53979150
## 4154 -0.6932043 6.104687e+00 -0.23799772
## 19981 -0.6932043 -1.026200e-01 1.34225936
## 32794 0.1037272 -1.215766e-01 1.93485577
## 17962 0.9006588 -1.215766e-01 -0.43552986
## 8482 2.4945218 -1.215766e-01 1.93485577
## 8053 1.6975903 -1.208284e-01 -0.63306200
## 891 0.9006588 -1.215766e-01 -1.22565841
## 22454 -0.6932043 -1.215766e-01 0.74966296
## 29474 -0.6932043 -1.215766e-01 -0.63306200
## 29960 0.9006588 -1.215766e-01 -0.63306200
## 26415 0.9006588 -1.215766e-01 -0.23799772
## 14079 -0.6932043 -1.215766e-01 -0.23799772
## 29256 -0.6932043 -1.215766e-01 -0.04046559
## 66 -0.6932043 -1.215766e-01 0.15706655
## 15983 0.9006588 -1.215766e-01 0.15706655
## 11199 -0.6932043 -1.215766e-01 -0.43552986
## 19773 0.9006588 -1.215766e-01 0.15706655
## 2349 2.4945218 -1.215766e-01 1.14472723
## 27774 0.9006588 -1.215766e-01 -0.23799772
## 29012 -0.6932043 1.269800e-01 -0.04046559
## 16394 0.1037272 -1.215766e-01 -0.43552986
## 21577 3.2914534 -1.215766e-01 0.15706655
## 19374 -0.6932043 -1.215766e-01 0.74966296
## 18197 -0.6932043 -1.215766e-01 -0.04046559
## 9251 1.6975903 -1.215766e-01 0.55213082
## 19587 0.9006588 -1.215766e-01 1.93485577
## 30977 0.9006588 -1.215766e-01 0.55213082
## 10532 0.1037272 -1.215766e-01 0.74966296
## 32795 -0.6932043 -1.215766e-01 1.34225936
## 25503 -0.6932043 -1.215766e-01 -0.23799772
## 29590 0.1037272 -1.215766e-01 0.74966296
## 3832 -0.6932043 -1.215766e-01 -0.83059413
## 5012 0.9006588 -1.215766e-01 -1.02812627
## 32994 -0.6932043 -1.215766e-01 -1.22565841
## 9885 -0.6932043 4.235520e-01 -1.02812627
## 5614 -0.6932043 -1.215766e-01 -0.63306200
## 29725 -0.6932043 -1.215766e-01 0.74966296
## 24280 -0.6932043 -1.215766e-01 -1.02812627
## 31125 -0.6932043 -1.215766e-01 0.74966296
## 10767 -0.6932043 -1.215766e-01 0.55213082
## 12792 -0.6932043 -1.215766e-01 0.74966296
## 8475 -0.6932043 -1.215766e-01 -0.43552986
## 16865 -0.6932043 -1.215766e-01 -0.63306200
## 27577 -0.6932043 -1.215766e-01 -1.02812627
## 25572 -0.6932043 -1.215766e-01 -0.83059413
## 151 0.1037272 -1.215766e-01 0.15706655
## 32652 -0.6932043 -1.215766e-01 -0.04046559
## 25887 -0.6932043 -1.215766e-01 -1.02812627
## 2223 0.9006588 -1.215766e-01 -0.63306200
## 19433 -0.6932043 -1.215766e-01 -0.04046559
## 33086 0.1037272 -1.215766e-01 -0.23799772
## 6992 -0.6932043 -1.215766e-01 0.55213082
## 9943 -0.6932043 -4.088614e-02 -0.63306200
## 23683 0.9006588 -1.215766e-01 -1.22565841
## 11260 -0.6932043 -1.215766e-01 -0.43552986
## 33006 -0.6932043 -1.215766e-01 -0.04046559
## 7940 0.1037272 -7.605572e-02 -0.83059413
## 4806 0.1037272 -1.215766e-01 0.74966296
## 4407 -0.6932043 5.633573e-01 -1.02812627
## 8697 1.6975903 -2.554621e-02 -0.23799772
## 21670 0.1037272 -1.215766e-01 0.74966296
## 5016 -0.6932043 1.069494e+01 -0.63306200
## 15078 -0.6932043 -1.215766e-01 0.55213082
## 26433 -0.6932043 -1.215766e-01 -1.02812627
## 13317 -0.6932043 -1.215766e-01 -1.62072268
## 4044 -0.6932043 -1.215766e-01 0.15706655
## 9410 0.9006588 1.775368e+01 -1.81825481
## 29252 0.9006588 -1.215766e-01 0.55213082
## 8238 0.1037272 -1.215766e-01 -0.43552986
## 10347 0.1037272 -1.215766e-01 -0.04046559
## 33083 -0.6932043 -1.215766e-01 -0.04046559
## 5187 -0.6932043 -7.493329e-02 -0.83059413
## 10993 -0.6932043 -1.215766e-01 0.55213082
## 13198 1.6975903 -1.215766e-01 -0.23799772
## 18609 -0.6932043 -1.215766e-01 -1.02812627
## 44 -0.6932043 -1.215766e-01 1.14472723
## 20528 0.1037272 -1.215766e-01 -1.22565841
## 26807 0.1037272 -1.215766e-01 0.35459868
## 24792 -0.6932043 -1.215766e-01 -0.23799772
## 10028 -0.6932043 -1.215766e-01 0.94719509
## 26819 0.1037272 -1.044907e-01 -0.43552986
## 23791 -0.6932043 -1.215766e-01 -0.83059413
## 29119 -0.6932043 -1.215766e-01 0.55213082
## 4363 -0.6932043 -1.215766e-01 -0.83059413
## 31089 1.6975903 -1.215766e-01 0.55213082
## 33211 0.1037272 -1.215766e-01 -0.23799772
## 4672 -0.6932043 -1.215766e-01 1.14472723
## 28324 0.1037272 -1.215766e-01 -0.43552986
## 28701 -0.6932043 -1.215766e-01 0.94719509
## 14051 -0.6932043 -1.215766e-01 -1.02812627
## 26578 -0.6932043 -1.215766e-01 -0.43552986
## open_credit_lines bankcard_utilization total_open_revolving_accounts
## 22546 0.14699028 1.05917467 0.888852944
## 29151 -1.11550049 -2.17106754 -1.350738221
## 18166 0.56782053 -1.34556119 0.216975595
## 20108 -0.69467024 1.20274099 0.216975595
## 21654 0.77823566 -0.23292221 0.440934711
## 10433 0.35740540 -1.20199487 -0.006983522
## 11330 -1.32591562 1.31041573 -1.126779105
## 10690 -0.69467024 0.62847571 -0.230942638
## 19147 1.40948105 -0.84307907 0.216975595
## 18225 -0.69467024 -2.06339280 -0.454901755
## 30110 -0.90508537 0.48490939 -0.902819988
## 27870 -1.11550049 0.37723465 -0.678860871
## 27498 -0.06342485 1.27452415 -0.902819988
## 19997 1.83031130 1.09506625 1.560730294
## 17368 -0.27383998 0.91560835 -0.006983522
## 20599 0.98865079 0.23366833 1.336771177
## 14673 0.35740540 1.05917467 0.440934711
## 24600 -0.69467024 0.62847571 -0.902819988
## 8447 -0.69467024 1.09506625 -1.350738221
## 27908 -0.90508537 1.20274099 -0.006983522
## 12890 3.30321720 -1.70447700 5.144076158
## 28529 -0.90508537 0.66436729 -0.678860871
## 2533 2.46155669 -0.30470537 2.008648527
## 24376 1.19906592 0.01831885 0.888852944
## 6241 -0.48425511 -0.26881379 -0.678860871
## 564 -0.06342485 0.52080097 0.216975595
## 20961 -0.90508537 1.23863257 -0.902819988
## 21541 -0.27383998 -0.91486223 0.216975595
## 13045 -0.48425511 -0.37648853 -1.126779105
## 13215 -0.27383998 -1.30966961 -0.678860871
## 13 0.77823566 0.73615045 0.664893828
## 9141 2.46155669 0.01831885 1.336771177
## 2979 -0.27383998 1.09506625 -0.006983522
## 4648 -0.69467024 -0.37648853 -0.678860871
## 4671 0.56782053 1.31041573 -0.678860871
## 31959 -0.90508537 -1.16610329 -1.574697338
## 7316 0.14699028 -1.56091067 -0.230942638
## 13792 0.35740540 0.87971677 0.888852944
## 20140 -0.48425511 -2.06339280 -0.902819988
## 1565 -0.06342485 -1.41734435 0.216975595
## 26701 1.61989617 0.44901781 1.336771177
## 16556 -0.90508537 1.41809047 -0.902819988
## 18104 -0.27383998 -0.26881379 -0.902819988
## 11927 -0.48425511 -1.63269384 -0.006983522
## 9144 -0.48425511 1.20274099 -0.230942638
## 26961 0.35740540 -1.30966961 0.216975595
## 32960 -0.90508537 0.73615045 -0.678860871
## 1527 -0.90508537 1.34630731 -0.454901755
## 14725 -0.90508537 -1.91982648 -1.126779105
## 29171 -0.48425511 0.66436729 -0.454901755
## 1634 0.77823566 0.05421043 1.336771177
## 12257 -1.32591562 -1.30966961 -0.678860871
## 32905 -1.32591562 0.62847571 -1.126779105
## 15486 -0.48425511 0.59258413 -0.454901755
## 26180 0.35740540 -0.23292221 0.440934711
## 1571 1.19906592 -0.77129591 1.560730294
## 8986 0.56782053 0.87971677 -0.230942638
## 22031 1.19906592 -0.12524747 1.336771177
## 20038 -0.06342485 -0.48416327 0.440934711
## 26910 -0.90508537 0.55669255 -0.902819988
## 31831 -0.06342485 0.55669255 0.216975595
## 15755 -1.53633075 1.34630731 -1.350738221
## 1109 0.77823566 0.05421043 0.440934711
## 19312 0.14699028 -0.91486223 -0.230942638
## 32657 0.14699028 -0.87897065 -0.006983522
## 29357 2.04072643 0.30545149 2.232607643
## 786 -0.90508537 -1.56091067 -0.902819988
## 205 2.25114156 -0.23292221 -0.454901755
## 16246 0.14699028 -2.17106754 -1.574697338
## 304 -0.69467024 -1.99160964 -0.230942638
## 12386 -1.11550049 0.30545149 -0.902819988
## 4221 -0.90508537 -0.01757273 -0.902819988
## 21987 0.77823566 0.44901781 0.216975595
## 23894 1.40948105 0.52080097 1.560730294
## 10046 -0.48425511 1.38219889 -0.454901755
## 14360 -0.06342485 1.13095783 -0.006983522
## 23419 -0.27383998 1.34630731 -0.678860871
## 8135 -0.27383998 0.34134307 -0.902819988
## 2208 0.14699028 -0.05346431 0.664893828
## 30912 -0.69467024 0.73615045 -0.678860871
## 30271 0.35740540 -1.02253697 1.112812061
## 22503 -0.48425511 -0.19703063 -0.006983522
## 4267 0.35740540 -1.30966961 0.888852944
## 384 0.14699028 0.77204203 -0.230942638
## 14462 -0.69467024 0.44901781 -0.678860871
## 3422 0.35740540 0.52080097 0.664893828
## 13610 -1.11550049 -1.38145277 -0.902819988
## 11448 0.56782053 -0.69951275 0.216975595
## 20580 1.61989617 -0.44827169 -0.678860871
## 19659 0.56782053 -0.05346431 -0.006983522
## 18694 0.77823566 0.37723465 0.888852944
## 4560 -0.27383998 1.34630731 -0.006983522
## 27525 0.35740540 1.23863257 -0.006983522
## 4850 0.98865079 -0.26881379 0.888852944
## 26882 -0.06342485 -1.91982648 0.216975595
## 12047 -0.69467024 0.30545149 -0.678860871
## 6009 0.35740540 -0.44827169 0.216975595
## 14304 -0.48425511 -1.13021171 -0.454901755
## 20906 -0.90508537 0.66436729 -0.902819988
## 29701 0.35740540 -0.73540433 0.888852944
## 7896 0.56782053 -2.09928438 -0.678860871
## 4314 0.77823566 -0.26881379 1.112812061
## 182 0.56782053 -1.13021171 0.440934711
## 24072 -0.48425511 -0.30470537 -0.006983522
## 23514 0.35740540 0.34134307 -0.006983522
## 7988 1.61989617 0.87971677 1.560730294
## 22230 -0.48425511 1.02328309 -0.678860871
## 28548 1.19906592 -0.69951275 1.560730294
## 17266 -0.48425511 0.80793361 -0.454901755
## 4992 -0.48425511 0.91560835 -0.678860871
## 28311 -0.69467024 -1.16610329 -0.902819988
## 32130 -0.27383998 -1.95571806 0.216975595
## 16555 -0.69467024 1.34630731 -0.902819988
## 13112 -0.27383998 -1.38145277 -0.006983522
## 8512 -0.90508537 0.34134307 -0.678860871
## 21423 0.35740540 0.73615045 0.440934711
## 1351 -0.48425511 0.23366833 -0.230942638
## 24579 -0.69467024 0.30545149 -0.454901755
## 16841 -0.69467024 1.16684941 -0.902819988
## 21322 1.40948105 1.05917467 1.560730294
## 10222 -0.48425511 -0.87897065 -0.006983522
## 30540 -0.48425511 1.20274099 -0.902819988
## 8141 -0.48425511 0.55669255 -0.902819988
## 17289 -0.06342485 0.16188517 0.216975595
## 5551 -0.90508537 -1.66858542 -0.678860871
## 6263 3.09280207 0.41312623 3.352403226
## 1461 0.56782053 -1.30966961 0.440934711
## 11417 -1.11550049 -0.84307907 -0.454901755
## 1820 -0.27383998 1.02328309 -0.230942638
## 910 -0.27383998 -0.08935589 -0.006983522
## 13932 -1.11550049 -0.12524747 -0.454901755
## 22830 0.35740540 0.37723465 0.664893828
## 11028 -0.06342485 -0.84307907 -0.454901755
## 4509 -1.32591562 1.31041573 -1.574697338
## 27739 2.88238694 0.44901781 3.128444109
## 14806 -0.48425511 0.98739151 -0.902819988
## 29144 0.98865079 -0.41238011 1.336771177
## 11281 -0.90508537 -0.19703063 -0.678860871
## 15296 -0.27383998 0.37723465 0.216975595
## 393 1.61989617 1.09506625 -0.006983522
## 1231 -0.90508537 -0.98664539 -1.126779105
## 23821 0.77823566 0.44901781 0.216975595
## 15359 -0.48425511 1.20274099 -0.454901755
## 28281 0.35740540 -1.20199487 0.440934711
## 1340 -1.32591562 0.95149993 -1.350738221
## 6861 -1.32591562 0.62847571 -1.350738221
## 4511 -0.27383998 -2.13517596 -0.678860871
## 19540 1.83031130 -1.77626016 2.008648527
## 1364 -0.90508537 0.62847571 -0.902819988
## 30479 -0.69467024 1.20274099 -0.454901755
## 27183 -0.48425511 -0.30470537 -0.454901755
## 20253 -0.48425511 -1.30966961 -0.454901755
## 16593 -0.69467024 -1.20199487 -0.678860871
## 12460 -0.90508537 0.23366833 -0.678860871
## 13328 0.35740540 0.44901781 0.888852944
## 27369 2.04072643 0.59258413 1.784689410
## 18795 0.56782053 -1.52501909 0.888852944
## 1988 -0.69467024 -1.59680225 -0.678860871
## 8530 -0.27383998 1.23863257 -0.902819988
## 6637 -0.27383998 -0.19703063 -0.678860871
## 10798 -0.27383998 -0.44827169 -1.126779105
## 8563 1.40948105 -1.95571806 1.784689410
## 3713 -0.27383998 0.66436729 -0.454901755
## 25752 0.35740540 -1.20199487 0.440934711
## 29102 0.56782053 -0.69951275 1.560730294
## 13660 -0.48425511 0.48490939 -0.230942638
## 14985 2.46155669 -0.77129591 1.336771177
## 12203 -1.11550049 -2.17106754 -1.574697338
## 10146 -0.48425511 1.05917467 -0.006983522
## 4000 -1.32591562 0.91560835 -1.126779105
## 33049 0.14699028 0.87971677 -0.006983522
## 20895 1.61989617 -1.09432013 2.456566760
## 10302 -0.48425511 0.59258413 -0.678860871
## 11539 -0.48425511 -0.91486223 -0.006983522
## 12556 -0.69467024 -0.62772959 -0.454901755
## 26056 -0.90508537 -2.02750122 -0.902819988
## 26127 -0.90508537 1.20274099 -0.902819988
## 13645 1.83031130 -0.23292221 0.888852944
## 31945 0.77823566 -0.87897065 0.888852944
## 9061 4.98653823 -0.62772959 1.336771177
## 16010 -0.69467024 0.91560835 -1.126779105
## 8736 -0.69467024 -1.70447700 -0.902819988
## 9958 -0.48425511 -2.17106754 -0.678860871
## 11617 0.98865079 -2.06339280 0.888852944
## 12010 -0.06342485 -0.34059695 0.216975595
## 12514 -0.90508537 0.30545149 -0.902819988
## 2974 0.98865079 1.27452415 1.560730294
## 9668 0.35740540 -1.20199487 0.440934711
## 16044 -1.11550049 0.84382519 -0.678860871
## 11655 -0.27383998 0.01831885 -0.006983522
## 6818 0.77823566 0.55669255 1.560730294
## 29018 -0.69467024 -1.34556119 -0.678860871
## 4341 1.61989617 -0.87897065 -0.006983522
## 23949 -0.69467024 0.87971677 -0.678860871
## 26561 0.35740540 0.87971677 0.216975595
## 18863 1.61989617 -1.30966961 0.216975595
## 20650 -1.74674588 -1.34556119 -1.350738221
## 4730 -0.27383998 0.12599359 0.216975595
## 25485 -0.69467024 -2.02750122 -0.454901755
## 16381 -1.11550049 -1.95571806 -1.126779105
## 18004 0.35740540 -0.16113905 0.664893828
## 24164 -1.53633075 1.20274099 -1.126779105
## 25422 -0.48425511 0.44901781 -0.006983522
## 9266 -0.48425511 1.05917467 -0.454901755
## 5937 -0.90508537 1.34630731 -0.902819988
## 11357 2.46155669 -0.44827169 1.336771177
## 1275 -0.27383998 -0.41238011 -0.006983522
## 3169 -1.11550049 1.23863257 -0.678860871
## 30457 2.46155669 1.05917467 2.904484993
## 27615 -1.11550049 0.34134307 -1.126779105
## 25322 -0.48425511 -0.37648853 -0.454901755
## 6970 0.14699028 0.95149993 0.440934711
## 8939 3.09280207 0.84382519 3.352403226
## 12696 0.98865079 -1.84804332 0.888852944
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## 10993 0.77823566 0.48490939 0.888852944
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## 4672 1.40948105 0.05421043 2.456566760
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## 14051 -0.90508537 0.59258413 -1.126779105
## 26578 -0.69467024 1.13095783 -0.454901755
## installment_balance real_estate_balance revolving_balance
## 22546 -2.489213e-01 -6.805414e-01 -0.1548260692
## 29151 -1.518500e-01 -3.278006e-01 -0.6290596561
## 18166 -2.667072e-01 7.339482e-01 -0.4475675388
## 20108 -1.942900e-01 -6.805414e-01 -0.4918014963
## 21654 -3.423185e-01 2.119533e+00 0.1860728276
## 10433 -1.179481e-01 3.882916e-01 -0.0489240266
## 11330 -6.425714e-01 1.680888e+00 -0.5535961450
## 10690 -6.425714e-01 -6.805414e-01 -0.2275051825
## 19147 1.984834e-01 5.454509e-01 -0.2885720908
## 18225 -2.797127e-01 -6.805414e-01 -0.6248514184
## 30110 -3.832971e-01 -6.805414e-01 0.8720472180
## 27870 -6.425714e-01 -6.805414e-01 -0.4457007266
## 27498 1.986662e+00 2.525513e-01 -0.3294837554
## 19997 -1.951041e-01 -1.835804e-01 0.1775297886
## 17368 -6.425714e-01 1.482336e+00 0.1674996280
## 20599 7.356415e-02 -6.805414e-01 -0.0005451132
## 14673 -5.868546e-01 1.573768e+00 -0.2038694412
## 24600 -2.633463e-01 -4.292387e-02 -0.3734329449
## 8447 8.752059e-01 4.952261e-01 -0.5713150407
## 27908 2.425517e-01 9.371264e-01 0.7683916782
## 12890 1.383618e-01 5.131927e-01 -0.1326141678
## 28529 -9.880522e-02 -6.805414e-01 -0.2581019184
## 2533 1.710321e-01 2.640489e+00 9.3905012455
## 24376 -3.704169e-01 -3.602565e-01 0.7178928255
## 6241 -6.680301e-02 7.064395e-01 -0.4325381183
## 564 -6.425714e-01 4.899887e-01 0.0495107672
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## 13215 2.782583e-02 -6.805414e-01 0.0098647381
## 13 -1.340223e-01 1.189232e-01 0.0248625176
## 9141 1.715843e+00 -6.805414e-01 0.5599415267
## 2979 -4.216246e-01 -6.805414e-01 -0.2921158700
## 4648 -2.604654e-01 1.051608e+00 -0.2840474443
## 4671 9.447215e-01 3.039816e+00 1.2454413039
## 31959 6.249708e-01 -6.805414e-01 -0.6149478213
## 7316 -3.266618e-01 -2.679810e-03 -0.2980643564
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## 1565 -3.480080e-02 5.746896e+00 -0.1201160182
## 26701 3.648615e-01 -1.564363e-02 1.3611520209
## 16556 -3.408154e-01 -6.805414e-01 -0.0853743263
## 18104 -5.451244e-01 1.057747e+00 -0.4726904016
## 11927 -6.425714e-01 -6.805414e-01 -0.0498099714
## 9144 -3.682250e-01 7.694057e-01 0.6791643820
## 26961 -3.773476e-01 9.416226e-01 0.1289926707
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## 21577 5.577933e-01 -6.805414e-01 -0.5003128944
## 19374 2.455160e-01 5.054848e-01 0.0826387740
## 18197 -2.868939e-01 1.837794e-01 -0.4188692560
## 9251 2.666212e-01 2.050317e+00 3.9713035610
## 19587 1.493006e-01 1.196514e+00 -0.2658222944
## 30977 -4.557562e-01 1.340388e+00 -0.1925736452
## 10532 -4.044441e-01 -6.805414e-01 0.2515694599
## 32795 -4.314988e-01 -2.019237e-01 0.3498460492
## 25503 8.504570e-02 -6.805414e-01 -0.4692099043
## 29590 -1.066753e-01 2.814064e-01 0.3254192858
## 3832 -4.659548e-02 -6.805414e-01 -0.3241997275
## 5012 -4.840425e-01 5.643747e-01 0.1469963344
## 32994 -3.480383e-01 3.963268e-01 -0.5167977955
## 9885 -5.729723e-01 -5.289530e-01 -0.4141864050
## 5614 -5.841199e-01 1.725078e-01 -0.4127625651
## 29725 -1.119359e-01 3.268817e-01 1.4576883616
## 24280 -1.334586e-01 -6.805414e-01 -0.4478206659
## 31125 -2.549960e-01 3.471421e+00 2.7489845261
## 10767 2.354331e-01 -6.805414e-01 0.6984336811
## 12792 -3.947161e-01 -4.730278e-02 -0.2608546754
## 8475 -5.339142e-01 1.878310e-01 -0.6225416338
## 16865 -5.822202e-01 5.604958e-02 -0.3975749402
## 27577 -4.371978e-01 -1.924803e-01 -0.4408280303
## 25572 -3.865120e-01 -6.671267e-01 -0.4331076543
## 151 1.715363e+00 1.781837e-01 -0.2517737414
## 32652 -6.425714e-01 -6.805414e-01 -0.3721040277
## 25887 -1.823074e-01 -6.805414e-01 0.4643860535
## 2223 -3.413164e-01 -6.805414e-01 -0.3657125689
## 19433 -6.425714e-01 4.278995e-01 2.1061050213
## 33086 -2.378781e-01 -6.805414e-01 -0.2165890771
## 6992 -6.096089e-01 3.067967e-01 1.3760232369
## 9943 -1.231879e-01 -6.805414e-01 -0.4248177424
## 23683 -4.515811e-01 -6.805414e-01 -0.6174790922
## 11260 -4.547333e-01 7.739946e-01 -0.0170616552
## 33006 -3.882029e-01 -6.805414e-01 -0.2748715876
## 7940 -6.349518e-01 2.224732e-01 -0.2349407905
## 4806 -4.178670e-01 -6.805414e-01 -0.3927655257
## 4407 -2.101554e-01 -6.805414e-01 -0.4262732231
## 8697 2.982894e-01 -6.805414e-01 -0.6201685674
## 21670 1.603115e-02 -6.805414e-01 0.1510463677
## 5016 -6.425714e-01 -6.805414e-01 -0.2878443505
## 15078 3.464073e-03 3.185562e-01 -0.3205926666
## 26433 -1.553153e-01 3.195009e-02 -0.4500038870
## 13317 -3.711267e-01 -6.805414e-01 -0.5285049231
## 4044 -6.391687e-01 -6.805414e-01 -0.0828114146
## 9410 -5.604679e-01 1.144424e+00 0.1522803622
## 29252 -5.402604e-01 -7.410772e-03 -0.2162410273
## 8238 -6.425714e-01 -2.596279e-01 -0.5585321231
## 10347 -3.821907e-01 -6.805414e-01 -0.3054050417
## 33083 -6.425714e-01 -6.805414e-01 1.0916982428
## 5187 -6.315074e-01 -6.805414e-01 -0.4067191561
## 10993 -6.275410e-01 1.775328e+00 7.9998843423
## 13198 -6.425714e-01 1.576578e+00 1.4839502963
## 18609 -5.718033e-01 -6.805414e-01 -0.5161016961
## 44 1.327141e+00 7.659038e-01 -0.0769262099
## 20528 1.145857e+00 1.960725e+00 -0.6247881366
## 26807 9.396070e-01 -6.805414e-01 -0.1009100009
## 24792 6.970749e-01 -6.805414e-01 -0.4421885883
## 10028 2.262896e-01 6.881888e-01 0.3300704960
## 26819 9.357033e-01 8.018369e-01 -0.4730384514
## 23791 -5.209505e-01 -6.805414e-01 -0.2222211546
## 29119 4.317467e-01 -6.805414e-01 -0.3634977069
## 4363 -4.021060e-01 -6.805414e-01 -0.4749052636
## 31089 -1.076565e-01 2.158826e+00 0.4410350802
## 33211 -6.026574e-01 -6.805414e-01 -0.3287243741
## 4672 -3.365568e-01 -6.805414e-01 -0.0613588944
## 28324 1.035300e-02 3.769706e-01 -0.3027788483
## 28701 4.310370e-01 6.810183e-01 -0.2067804027
## 14051 -8.390008e-02 4.241814e-01 -0.2059260988
## 26578 -5.854977e-01 -6.805414e-01 -0.1442896545
## real_estate_payment revolving_available_percent total_inquiries
## 22546 -0.3712356160 0.118751807 0.18009347
## 29151 -0.0746789545 1.451310401 -0.35432131
## 18166 0.3309964785 1.249407584 -0.08711392
## 20108 -0.3712356160 -0.406195517 2.31775257
## 21654 1.1625871167 0.320654625 -0.62152870
## 10433 0.1739182469 1.087885330 -0.88873608
## 11330 0.9474295389 -1.779134674 -0.62152870
## 10690 -0.3712356160 -0.446576081 -0.62152870
## 19147 0.2381575237 1.209027020 -0.88873608
## 18225 -0.3712356160 2.057018852 -0.88873608
## 30110 -0.3712356160 -0.688859461 -0.88873608
## 27870 -0.3712356160 -0.729240025 -0.35432131
## 27498 0.1690783013 -0.285053827 -0.88873608
## 19997 0.2707171571 -1.375329040 -0.62152870
## 17368 -0.0311194449 -0.890762278 -0.88873608
## 20599 -0.3712356160 -0.244673263 -0.08711392
## 14673 0.5998334520 -1.496470730 -0.62152870
## 24600 0.2548773354 -1.011903969 -0.62152870
## 8447 0.0920791682 -1.536851293 0.18009347
## 27908 0.2535573503 -0.486956644 0.18009347
## 12890 0.4568350619 1.774354908 0.71450824
## 28529 -0.3712356160 -1.092665096 -0.88873608
## 2533 0.8629504899 -0.971523405 1.78333780
## 24376 0.1598384054 0.159132371 -0.35432131
## 6241 0.1822781527 0.239893498 -0.08711392
## 564 0.1224388264 -0.810001152 -0.35432131
## 20961 -0.3712356160 -1.698373547 -0.08711392
## 21541 -0.3712356160 -0.042770446 1.51613041
## 13045 0.6271131449 -0.285053827 -0.62152870
## 13215 -0.3712356160 1.451310401 -0.62152870
## 13 0.0005601985 -0.608098334 -0.35432131
## 9141 -0.3712356160 -0.285053827 0.44730086
## 2979 -0.3712356160 -0.648478898 -0.88873608
## 4648 0.4973146062 -0.446576081 2.58495996
## 4671 1.6395417473 -1.577231857 0.44730086
## 31959 -0.3712356160 1.007124203 0.44730086
## 7316 -0.0231995340 0.764840822 0.44730086
## 13792 -0.3712356160 -1.456090167 -0.88873608
## 20140 -0.3712356160 2.016638289 -0.62152870
## 1565 2.1626958578 1.330168711 2.85216735
## 26701 -0.0579591427 -0.527337208 0.18009347
## 16556 -0.3712356160 -1.375329040 -0.88873608
## 18104 0.4264754036 1.047504767 -0.35432131
## 11927 -0.3712356160 0.724460259 -0.62152870
## 9144 0.4440752055 -1.415709603 -0.62152870
## 26961 0.5624338731 1.168646457 0.98171563
## 32960 0.2962368698 -1.698373547 -1.15594347
## 1527 -0.3712356160 -1.052284532 -0.35432131
## 14725 -0.3712356160 2.016638289 1.24892302
## 29171 0.4304353591 -0.204292700 0.71450824
## 1634 1.3293452394 0.724460259 2.31775257
## 12257 0.4449551956 1.612832655 -0.88873608
## 32905 0.2016379348 0.118751807 -0.62152870
## 15486 0.8185109902 -0.971523405 -0.35432131
## 26180 -0.3712356160 -0.608098334 -1.15594347
## 1571 -0.3712356160 0.562938005 -0.62152870
## 8986 -0.3712356160 -0.971523405 1.78333780
## 22031 -0.3712356160 0.118751807 -0.88873608
## 20038 -0.2845565918 0.320654625 -0.08711392
## 26910 -0.3712356160 -0.931142842 0.98171563
## 31831 -0.3712356160 -0.244673263 2.05054518
## 15755 -0.3712356160 -1.819515238 -0.08711392
## 1109 0.5303142347 0.320654625 -0.62152870
## 19312 -0.3712356160 0.926363076 2.58495996
## 32657 -0.3712356160 0.603318569 -0.62152870
## 29357 0.4801547993 -0.527337208 0.18009347
## 786 0.2720371423 1.451310401 1.78333780
## 205 -0.3712356160 -0.042770446 -0.08711392
## 16246 -0.1389182313 -1.819515238 -1.15594347
## 304 0.0401597527 1.047504767 -0.88873608
## 12386 -0.3712356160 -0.608098334 -0.88873608
## 4221 0.3274765181 -0.083151010 -0.62152870
## 21987 0.3719160179 -0.486956644 -0.08711392
## 23894 -0.3712356160 -0.810001152 -0.88873608
## 10046 0.2878769639 0.199512934 -0.62152870
## 14360 0.2997568302 -0.446576081 -0.08711392
## 23419 0.0260799112 -1.738754111 -1.15594347
## 8135 -0.0324394300 -0.729240025 -0.62152870
## 2208 -0.3712356160 1.087885330 0.44730086
## 30912 0.0625995001 -0.486956644 -0.35432131
## 30271 -0.3712356160 1.087885330 2.05054518
## 22503 -0.3712356160 0.603318569 -0.08711392
## 4267 -0.3712356160 1.370549274 1.51613041
## 384 -0.3712356160 -1.173426223 -0.62152870
## 14462 0.4475951659 -0.648478898 -0.08711392
## 3422 -0.3712356160 -0.810001152 -0.62152870
## 13610 -0.3712356160 1.370549274 -0.35432131
## 11448 0.4744348637 0.522557442 0.71450824
## 20580 -0.3712356160 1.128265893 -0.35432131
## 19659 0.4009556909 -0.204292700 0.98171563
## 18694 -0.3712356160 -0.567717771 0.71450824
## 4560 -0.3712356160 -0.406195517 -0.88873608
## 27525 0.1365186679 -0.769620588 -0.08711392
## 4850 0.8589905345 0.199512934 0.44730086
## 26882 0.3952357553 1.935877162 0.18009347
## 12047 -0.3712356160 0.159132371 -0.88873608
## 6009 -0.0047197421 0.118751807 -0.88873608
## 14304 -0.3712356160 1.007124203 -0.62152870
## 20906 -0.3712356160 -0.810001152 -0.88873608
## 29701 0.3019568054 1.087885330 -0.35432131
## 7896 -0.3712356160 2.057018852 3.65378951
## 4314 -0.3712356160 0.562938005 -0.88873608
## 182 0.2469574246 1.128265893 -1.15594347
## 24072 -0.3712356160 0.401415752 -0.88873608
## 23514 0.4797148043 -0.446576081 -0.62152870
## 7988 0.0815192871 -0.931142842 0.98171563
## 22230 -0.3712356160 1.128265893 -0.62152870
## 28548 -0.0830388604 0.764840822 -0.88873608
## 17266 -0.2335171664 -0.446576081 -0.88873608
## 4992 1.5810224061 0.401415752 0.18009347
## 28311 -0.3712356160 1.289788147 -0.62152870
## 32130 -0.3712356160 1.895496599 -0.08711392
## 16555 0.4955546260 -1.819515238 -0.62152870
## 13112 -0.3712356160 1.168646457 0.18009347
## 8512 -0.3712356160 -0.688859461 -0.08711392
## 21423 -0.3712356160 0.441796315 -0.08711392
## 1351 -0.3712356160 0.320654625 -0.35432131
## 24579 -0.3712356160 -0.648478898 -1.15594347
## 16841 -0.3712356160 -1.294567913 -0.35432131
## 21322 0.0533596041 -0.769620588 -0.08711392
## 10222 -0.3712356160 0.441796315 -0.08711392
## 30540 0.2517973701 -0.527337208 -0.08711392
## 8141 0.0788793168 0.239893498 -0.35432131
## 17289 -0.3712356160 -0.365814954 -1.15594347
## 5551 -0.3712356160 1.572452091 -1.15594347
## 6263 1.0794280529 -0.325434390 -0.88873608
## 1461 0.1549984599 1.249407584 0.44730086
## 11417 -0.3712356160 1.289788147 -0.62152870
## 1820 0.9087099748 -1.577231857 -0.35432131
## 910 0.3749959832 0.239893498 -0.08711392
## 13932 -0.3712356160 0.643699132 0.98171563
## 22830 -0.3712356160 -0.042770446 -0.08711392
## 11028 0.1008790691 0.724460259 -0.62152870
## 4509 0.6279931350 -1.779134674 -0.08711392
## 27739 0.2799570531 0.482176878 1.78333780
## 14806 -0.3712356160 -1.415709603 -0.62152870
## 29144 -0.3712356160 0.441796315 -0.88873608
## 11281 0.2130778060 -0.527337208 -0.88873608
## 15296 -0.3712356160 -1.173426223 0.71450824
## 393 0.1545584648 -0.042770446 -0.08711392
## 1231 0.0903191880 1.047504767 -0.35432131
## 23821 0.1585184202 0.401415752 0.98171563
## 15359 0.3538762209 -0.406195517 1.51613041
## 28281 -0.0671990387 0.764840822 0.18009347
## 1340 -0.2497969831 -1.375329040 -0.08711392
## 6861 -0.3712356160 -1.011903969 -0.88873608
## 4511 0.1857981131 2.097399416 2.05054518
## 19540 -0.3712356160 1.774354908 2.05054518
## 1364 -0.2471570128 -1.011903969 -0.88873608
## 30479 -0.3712356160 0.684079696 5.52424123
## 27183 0.1250787967 1.330168711 1.24892302
## 20253 -0.3712356160 1.693593781 -0.62152870
## 16593 0.1241988066 1.209027020 -1.15594347
## 12460 -0.3712356160 -0.083151010 -0.08711392
## 13328 -0.0240795241 -0.406195517 -0.35432131
## 27369 0.0819592821 -0.567717771 0.98171563
## 18795 -0.3712356160 1.330168711 0.71450824
## 1988 -0.3712356160 1.330168711 -0.35432131
## 8530 1.6263418959 -1.294567913 3.65378951
## 6637 1.3302252295 -0.810001152 -1.15594347
## 10798 0.4207554680 0.199512934 -0.35432131
## 8563 0.0551195843 1.976257726 0.18009347
## 3713 1.1436673297 -1.092665096 -0.08711392
## 25752 0.3428763448 1.128265893 1.24892302
## 29102 -0.3712356160 1.007124203 -0.08711392
## 13660 -0.3712356160 -0.850381715 -0.88873608
## 14985 0.4128355572 0.401415752 -0.35432131
## 12203 -0.3712356160 0.926363076 -0.88873608
## 10146 -0.3712356160 0.037990681 0.44730086
## 4000 0.4238354334 -0.769620588 -0.08711392
## 33049 0.0652394703 -1.254187349 -0.35432131
## 20895 -0.3712356160 0.764840822 -0.35432131
## 10302 0.3736759980 -0.204292700 -0.08711392
## 11539 -0.3712356160 0.845601949 -0.08711392
## 12556 -0.1402382165 0.724460259 -0.88873608
## 26056 -0.3712356160 1.976257726 -0.35432131
## 26127 0.2632372413 -1.496470730 -0.62152870
## 13645 0.1294787471 0.361035188 2.31775257
## 31945 0.2258376624 0.280274061 -0.35432131
## 9061 0.0621595050 0.603318569 -0.88873608
## 16010 -0.3712356160 -1.334948476 -0.62152870
## 8736 0.0903191880 1.532071528 -0.35432131
## 9958 -0.3712356160 0.885982513 0.44730086
## 11617 0.3864358544 1.774354908 -0.35432131
## 12010 -0.3712356160 0.926363076 -0.08711392
## 12514 0.1835981379 -0.850381715 0.18009347
## 2974 -0.3712356160 -1.415709603 -1.15594347
## 9668 0.0199199805 1.209027020 3.92099690
## 16044 -0.3712356160 -0.083151010 -0.35432131
## 11655 0.1312387273 1.087885330 -0.35432131
## 6818 -0.3712356160 -0.527337208 -1.15594347
## 29018 -0.3712356160 0.441796315 -0.08711392
## 4341 -0.3712356160 0.684079696 0.71450824
## 23949 0.1356386778 -1.052284532 -0.35432131
## 26561 0.3235165627 -0.931142842 -0.62152870
## 18863 0.4423152253 1.209027020 -0.08711392
## 20650 -0.3712356160 1.249407584 -1.15594347
## 4730 0.3314364736 -0.365814954 -1.15594347
## 25485 -0.3712356160 1.895496599 0.18009347
## 16381 0.0652394703 1.532071528 0.18009347
## 18004 0.3754359782 0.037990681 -0.35432131
## 24164 -0.3712356160 -1.657992984 -1.15594347
## 25422 -0.3712356160 -0.365814954 -0.88873608
## 9266 -0.3712356160 0.320654625 -0.08711392
## 5937 0.3455163151 -1.698373547 -1.15594347
## 11357 0.0436797130 0.280274061 -0.62152870
## 1275 0.1171588858 0.441796315 -0.62152870
## 3169 -0.3712356160 -1.657992984 -0.35432131
## 30457 -0.3712356160 -0.971523405 4.18820429
## 27615 -0.0905187762 0.361035188 2.05054518
## 25322 0.1039590344 0.118751807 -0.88873608
## 6970 -0.0143996331 -0.527337208 0.44730086
## 8939 -0.3712356160 -0.769620588 -1.15594347
## 12696 0.2663172067 1.814735472 0.44730086
## 30693 -0.3712356160 -0.608098334 -0.62152870
## 32975 -0.0940387366 -1.657992984 -0.88873608
## 30476 -0.3712356160 1.289788147 1.78333780
## 5755 -0.3712356160 -0.688859461 0.18009347
## 16948 0.1343186927 -0.567717771 0.98171563
## 25129 0.0471996734 0.361035188 -0.35432131
## 3905 -0.3712356160 -0.002389883 0.71450824
## 4920 -0.3712356160 -0.083151010 1.78333780
## 19358 1.2893056902 -0.769620588 -0.62152870
## 13988 -0.0539991873 1.087885330 0.98171563
## 7870 -0.3712356160 0.562938005 -1.15594347
## 16861 0.0120000697 -0.042770446 0.18009347
## 9674 -0.3712356160 0.441796315 -0.08711392
## 29381 0.4656349628 -1.577231857 0.18009347
## 19042 -0.3712356160 -1.092665096 0.18009347
## 1839 0.3402363745 -0.810001152 -0.62152870
## 4432 0.6425129716 -1.052284532 0.18009347
## 1448 0.2271576475 -0.002389883 -0.88873608
## 30273 0.3983157207 0.482176878 0.18009347
## 24289 -0.3712356160 -0.446576081 -0.35432131
## 4262 -0.3712356160 0.037990681 3.38658212
## 23846 -0.3712356160 0.562938005 -1.15594347
## 4941 -0.3712356160 0.159132371 -0.62152870
## 5026 0.2108778308 -0.688859461 0.98171563
## 6726 -0.3712356160 -0.931142842 0.18009347
## 16663 0.2297976178 -1.052284532 -0.62152870
## 17181 -0.0267194944 0.522557442 0.71450824
## 1214 0.1079189899 0.159132371 -0.35432131
## 19686 0.1549984599 -1.738754111 0.44730086
## 16156 -0.3712356160 0.684079696 9.26514466
## 8757 -0.3712356160 0.199512934 -0.08711392
## 24439 -0.0676390338 -1.092665096 -0.88873608
## 32011 -0.3712356160 -0.244673263 -0.35432131
## 6767 0.9368696578 0.764840822 0.71450824
## 27998 -0.3712356160 0.522557442 -0.88873608
## 16357 -0.3712356160 -0.365814954 -0.08711392
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## 671 -0.1270383651 -0.648478898 -0.62152870
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## 7091 -0.3712356160 -1.415709603 -0.88873608
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## 1311 -0.3712356160 -1.819515238 -0.88873608
## 14772 1.2439862004 -0.285053827 2.58495996
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## 8063 -0.3712356160 0.441796315 3.11937474
## 1757 -0.3712356160 0.441796315 1.24892302
## 18073 0.2025179249 1.370549274 0.44730086
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## 1849 -0.3712356160 -0.244673263 -0.62152870
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## 9791 -0.3712356160 1.612832655 -0.62152870
## 21977 -0.3712356160 -0.083151010 1.24892302
## 12658 0.1536784747 -0.325434390 0.71450824
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## 6729 -0.3712356160 -0.567717771 0.98171563
## 4156 -0.3712356160 0.320654625 1.78333780
## 33217 0.2161577713 2.057018852 -0.62152870
## 30062 -0.3712356160 0.724460259 0.98171563
## 11769 1.4279041299 -0.567717771 -0.88873608
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## 2073 0.8550305791 -0.850381715 -0.88873608
## 18380 -0.3712356160 1.047504767 2.05054518
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## 28770 -0.3712356160 -0.163912137 2.05054518
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## 26981 0.2113178258 2.057018852 1.51613041
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## 16653 0.5140344179 -0.729240025 1.24892302
## 4154 -0.3712356160 0.562938005 -0.62152870
## 19981 1.2435462053 -0.083151010 -0.35432131
## 32794 0.0779993267 1.047504767 -0.35432131
## 17962 0.4797148043 0.522557442 -0.62152870
## 8482 0.2302376128 -0.648478898 0.71450824
## 8053 -0.3712356160 1.532071528 1.78333780
## 891 -0.3712356160 -0.285053827 1.24892302
## 22454 -0.3712356160 0.078371244 1.51613041
## 29474 0.1915180487 -0.244673263 0.18009347
## 29960 -0.3712356160 -0.204292700 -0.62152870
## 26415 -0.3712356160 -0.567717771 -0.35432131
## 14079 -0.3712356160 -0.729240025 -0.88873608
## 29256 0.0344398171 1.289788147 0.71450824
## 66 -0.3712356160 -0.608098334 -0.88873608
## 15983 0.0256399161 0.401415752 0.71450824
## 11199 0.0023201787 1.289788147 -0.35432131
## 19773 -0.3712356160 0.724460259 0.44730086
## 2349 0.0142000449 0.926363076 2.05054518
## 27774 -0.3712356160 -1.011903969 -0.35432131
## 29012 -0.0891987910 -0.002389883 -0.62152870
## 16394 -0.3712356160 -0.850381715 -0.62152870
## 21577 -0.3712356160 1.693593781 3.38658212
## 19374 0.6046733976 0.118751807 -0.62152870
## 18197 0.0757993515 -0.002389883 -0.88873608
## 9251 1.0459884294 -1.092665096 -0.08711392
## 19587 0.8501906336 0.764840822 1.51613041
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## 5012 0.3507962556 -1.859895801 -0.35432131
## 32994 0.2531173553 -1.536851293 -0.62152870
## 9885 0.3441963299 -0.486956644 0.18009347
## 5614 0.0375197824 -1.092665096 -0.08711392
## 29725 0.3666360773 -1.213806786 -0.88873608
## 24280 -0.3712356160 0.280274061 -0.88873608
## 31125 1.3460650512 -0.527337208 -0.62152870
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## 12792 -0.0073597124 0.401415752 -0.88873608
## 8475 0.1272787719 1.814735472 1.24892302
## 16865 0.1805181726 -0.648478898 0.44730086
## 27577 -0.1133985186 0.320654625 0.18009347
## 25572 -0.3052363590 -0.769620588 -0.62152870
## 151 0.0344398171 -0.567717771 0.18009347
## 32652 -0.3712356160 -0.527337208 -0.88873608
## 25887 -0.3712356160 -0.123531573 -0.88873608
## 2223 -0.3712356160 -0.002389883 1.24892302
## 19433 0.1219988314 -0.688859461 -0.08711392
## 33086 -0.3712356160 -1.415709603 -0.62152870
## 6992 0.1818381577 0.482176878 -0.88873608
## 9943 -0.3712356160 -0.042770446 -0.88873608
## 23683 -0.3712356160 1.532071528 0.18009347
## 11260 0.5650738434 0.199512934 -0.35432131
## 33006 -0.3712356160 0.845601949 -0.88873608
## 7940 0.0894391979 -1.738754111 -0.62152870
## 4806 -0.3712356160 0.239893498 0.44730086
## 4407 -0.3712356160 0.361035188 1.51613041
## 8697 -0.3712356160 1.007124203 0.18009347
## 21670 -0.3712356160 0.037990681 0.18009347
## 5016 -0.3712356160 -0.446576081 -0.35432131
## 15078 0.1479585391 -0.325434390 -0.08711392
## 26433 0.1527984846 0.482176878 -0.88873608
## 13317 -0.3712356160 -0.688859461 -1.15594347
## 4044 -0.3712356160 0.159132371 -0.62152870
## 9410 0.3947957603 1.733974345 0.18009347
## 29252 0.0713994010 0.603318569 1.24892302
## 8238 -0.1683978994 -0.083151010 -0.62152870
## 10347 -0.3712356160 -0.163912137 -0.88873608
## 33083 -0.3712356160 -1.738754111 -1.15594347
## 5187 -0.3712356160 -0.890762278 0.44730086
## 10993 1.1080277309 -0.810001152 -0.88873608
## 13198 0.7956312478 -0.527337208 0.18009347
## 18609 -0.3712356160 1.047504767 -0.08711392
## 44 0.2909569293 0.199512934 -0.62152870
## 20528 0.7824313964 0.603318569 0.71450824
## 26807 -0.3712356160 0.684079696 0.18009347
## 24792 -0.3712356160 -0.688859461 -0.88873608
## 10028 0.3600361516 1.209027020 0.18009347
## 26819 0.5580339226 1.532071528 3.38658212
## 23791 -0.3712356160 0.724460259 -0.08711392
## 29119 0.0889992029 0.401415752 0.98171563
## 4363 -0.3712356160 -0.204292700 0.98171563
## 31089 0.9254297866 -0.890762278 1.24892302
## 33211 -0.3712356160 0.603318569 -0.35432131
## 4672 -0.3712356160 -0.042770446 -1.15594347
## 28324 0.2874369689 0.603318569 0.18009347
## 28701 0.3908358049 1.451310401 0.71450824
## 14051 0.2249576723 -0.971523405 -1.15594347
## 26578 -0.3712356160 -1.173426223 -0.88873608
## total_trade_items satisfactory_accounts now_delinquent_derog
## 22546 0.26207214 -0.10088313 -0.2571348
## 29151 -1.17476130 -1.25366526 -0.2571348
## 18166 0.10242398 0.31082478 -0.2571348
## 20108 -0.05722418 -0.01854155 0.5668725
## 21654 -0.69581682 -0.51259103 -0.2571348
## 10433 0.34189622 0.47550794 -0.2571348
## 11330 1.37960925 0.39316636 -0.2571348
## 10690 -1.01511314 -0.84195736 -0.2571348
## 19147 0.34189622 0.39316636 -0.2571348
## 18225 -1.41423354 -1.25366526 -0.2571348
## 30110 -1.09493722 -1.00664052 0.5668725
## 27870 -0.85546498 -0.92429894 -0.2571348
## 27498 0.74101662 0.88721584 -0.2571348
## 19997 0.26207214 0.47550794 -0.2571348
## 17368 -0.85546498 -0.92429894 1.3908798
## 20599 0.82084070 0.96955743 -0.2571348
## 14673 -0.05722418 -0.51259103 1.3908798
## 24600 -0.69581682 -0.51259103 -0.2571348
## 8447 0.50154438 0.14614162 -0.2571348
## 27908 1.29978517 1.54594849 -0.2571348
## 12890 4.25327612 4.51024541 -0.2571348
## 28529 -0.61599274 -0.51259103 -0.2571348
## 2533 1.45943333 1.71063165 -0.2571348
## 24376 1.29978517 1.54594849 -0.2571348
## 6241 0.66119254 0.80487426 -0.2571348
## 564 -0.45634458 -0.26556629 -0.2571348
## 20961 -0.93528906 -1.08898210 -0.2571348
## 21541 -0.85546498 -0.75961577 -0.2571348
## 13045 1.14013701 1.05189901 -0.2571348
## 13215 0.34189622 0.55784952 -0.2571348
## 13 -0.77564090 -0.67727419 -0.2571348
## 9141 1.61908149 1.79297324 -0.2571348
## 2979 -0.53616866 -0.51259103 -0.2571348
## 4648 -0.29669642 -0.67727419 0.5668725
## 4671 0.98048885 0.80487426 -0.2571348
## 31959 -1.17476130 -1.33600684 -0.2571348
## 7316 -0.69581682 -0.59493261 -0.2571348
## 13792 -0.93528906 -0.75961577 -0.2571348
## 20140 -1.09493722 -0.92429894 -0.2571348
## 1565 -1.01511314 -0.92429894 -0.2571348
## 26701 1.37960925 1.62829007 -0.2571348
## 16556 -0.13704826 -0.01854155 -0.2571348
## 18104 0.34189622 0.55784952 -0.2571348
## 11927 -0.37652050 -0.18322471 -0.2571348
## 9144 0.26207214 0.39316636 0.5668725
## 26961 0.50154438 0.72253268 -0.2571348
## 32960 -1.01511314 -0.92429894 -0.2571348
## 1527 -0.13704826 -0.10088313 -0.2571348
## 14725 0.34189622 0.22848320 0.5668725
## 29171 0.74101662 0.96955743 -0.2571348
## 1634 1.53925741 1.79297324 -0.2571348
## 12257 -1.73352985 -1.74771475 0.5668725
## 32905 -1.49405762 -1.41834842 -0.2571348
## 15486 -0.53616866 -0.34790787 -0.2571348
## 26180 0.42172030 -0.59493261 -0.2571348
## 1571 0.66119254 0.88721584 -0.2571348
## 8986 -0.93528906 -0.75961577 -0.2571348
## 22031 0.34189622 0.47550794 -0.2571348
## 20038 -0.53616866 -0.43024945 0.5668725
## 26910 0.90066477 0.39316636 -0.2571348
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## months11 months12 months2 months3 months4 months5
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## 27870 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 27498 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 3.5333252
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## 19312 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 3.5333252
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## 23894 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 3.5333252
## 10046 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
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## 13317 -0.3766756 -0.4081401 -0.1853004 -0.2243801 4.0205958 -0.2829976
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## 9410 -0.3766756 2.4499490 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 29252 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 8238 -0.3766756 2.4499490 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 10347 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 33083 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
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## 24792 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
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## 26819 -0.3766756 2.4499490 -0.1853004 -0.2243801 -0.2487001 -0.2829976
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## 29119 -0.3766756 -0.4081401 -0.1853004 -0.2243801 4.0205958 -0.2829976
## 4363 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 31089 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
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## 4672 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 28324 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 28701 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 -0.2829976
## 14051 -0.3766756 -0.4081401 -0.1853004 -0.2243801 -0.2487001 3.5333252
## 26578 -0.3766756 -0.4081401 -0.1853004 4.4563776 -0.2487001 -0.2829976
## months6 months7 months8 months9 day.L day.Q
## 22546 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 29151 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.01736415 -1.1208203
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## 20108 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 21654 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 10433 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 11330 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 10690 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 19147 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 18225 -0.2983763 -0.310694 -0.3124916 3.1227451 0.71659842 -0.5171684
## 30110 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 27870 -0.2983763 -0.310694 3.1998377 -0.3202062 -0.01736415 -1.1208203
## 27498 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 19997 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 17368 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 20599 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 14673 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 24600 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 8447 -0.2983763 3.218352 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 12890 -0.2983763 3.218352 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 28529 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 6241 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
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## 20961 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 9141 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 2979 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 4648 3.3512129 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 4671 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 31959 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 7316 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 13792 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 20140 -0.2983763 -0.310694 -0.3124916 3.1227451 1.45056099 1.2937873
## 1565 -0.2983763 3.218352 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 26701 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 11927 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
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## 26961 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 32960 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 1527 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 32905 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
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## 26180 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
## 1571 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
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## 22031 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
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## 15755 -0.2983763 -0.310694 3.1998377 -0.3202062 1.45056099 1.2937873
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## 19312 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 32657 3.3512129 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
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## 23894 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 10046 -0.2983763 -0.310694 3.1998377 -0.3202062 0.71659842 -0.5171684
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## 23419 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
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## 30271 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 4267 -0.2983763 -0.310694 3.1998377 -0.3202062 1.45056099 1.2937873
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## 4560 -0.2983763 3.218352 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 26882 -0.2983763 3.218352 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 32130 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 8512 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
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## 1351 -0.2983763 3.218352 -0.3124916 -0.3202062 1.45056099 1.2937873
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## 16841 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 21322 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.01736415 -1.1208203
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## 30540 -0.2983763 -0.310694 3.1998377 -0.3202062 1.45056099 1.2937873
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## 17289 -0.2983763 3.218352 -0.3124916 -0.3202062 1.45056099 1.2937873
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## 4509 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 27739 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 14806 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
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## 15296 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 1364 -0.2983763 3.218352 -0.3124916 -0.3202062 1.45056099 1.2937873
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## 16593 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 12460 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 13328 -0.2983763 -0.310694 3.1998377 -0.3202062 0.71659842 -0.5171684
## 27369 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
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## 22919 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 27491 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 1134 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 18523 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 10310 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 8457 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 16794 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 6568 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 7162 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 21967 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 24115 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
## 25480 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 12713 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 26166 -0.2983763 3.218352 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 32859 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 14288 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 27699 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 30877 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 826 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 23595 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 28770 -0.2983763 -0.310694 3.1998377 -0.3202062 -0.75132672 -0.5171684
## 17418 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 13622 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 23546 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 7890 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 1943 -0.2983763 -0.310694 3.1998377 -0.3202062 -0.01736415 -1.1208203
## 32207 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 3352 -0.2983763 -0.310694 3.1998377 -0.3202062 1.45056099 1.2937873
## 14732 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 5588 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 24839 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 16476 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 11490 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 6351 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.75132672 -0.5171684
## 29684 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 23753 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 21179 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 26274 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 23149 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 32334 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 20081 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 11088 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 18392 -0.2983763 -0.310694 3.1998377 -0.3202062 0.71659842 -0.5171684
## 25678 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 23968 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 26981 -0.2983763 -0.310694 -0.3124916 3.1227451 1.45056099 1.2937873
## 6512 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 20100 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 14325 -0.2983763 3.218352 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 5710 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 9197 -0.2983763 3.218352 -0.3124916 -0.3202062 1.45056099 1.2937873
## 31441 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 27775 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
## 16653 -0.2983763 -0.310694 -0.3124916 3.1227451 1.45056099 1.2937873
## 4154 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 19981 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 32794 -0.2983763 3.218352 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 17962 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
## 8482 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 8053 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 891 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 22454 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 29474 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 29960 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 26415 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 14079 -0.2983763 -0.310694 3.1998377 -0.3202062 1.45056099 1.2937873
## 29256 -0.2983763 3.218352 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 66 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 15983 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 11199 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 19773 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 2349 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 27774 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 29012 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 16394 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 21577 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 19374 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 18197 -0.2983763 3.218352 -0.3124916 -0.3202062 1.45056099 1.2937873
## 9251 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 19587 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 30977 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 10532 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 32795 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 25503 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 29590 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 3832 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 5012 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 32994 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 9885 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 5614 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 29725 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 24280 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 31125 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 10767 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 12792 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 8475 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 16865 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 27577 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.75132672 -0.5171684
## 25572 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 151 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 32652 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 25887 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 2223 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 19433 -0.2983763 -0.310694 3.1998377 -0.3202062 -1.48528929 1.2937873
## 33086 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 6992 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 9943 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 23683 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 11260 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 33006 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 7940 -0.2983763 -0.310694 3.1998377 -0.3202062 0.71659842 -0.5171684
## 4806 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 4407 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 8697 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 21670 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 5016 -0.2983763 -0.310694 3.1998377 -0.3202062 0.71659842 -0.5171684
## 15078 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 26433 -0.2983763 -0.310694 3.1998377 -0.3202062 -0.01736415 -1.1208203
## 13317 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 4044 -0.2983763 -0.310694 -0.3124916 -0.3202062 1.45056099 1.2937873
## 9410 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 29252 -0.2983763 3.218352 -0.3124916 -0.3202062 1.45056099 1.2937873
## 8238 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 10347 3.3512129 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 33083 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 5187 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 10993 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 13198 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 18609 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 44 -0.2983763 -0.310694 3.1998377 -0.3202062 -0.75132672 -0.5171684
## 20528 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 26807 -0.2983763 -0.310694 3.1998377 -0.3202062 -0.75132672 -0.5171684
## 24792 -0.2983763 3.218352 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## 10028 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 26819 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 23791 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 29119 -0.2983763 -0.310694 -0.3124916 -0.3202062 0.71659842 -0.5171684
## 4363 -0.2983763 -0.310694 -0.3124916 3.1227451 0.71659842 -0.5171684
## 31089 3.3512129 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 33211 -0.2983763 -0.310694 -0.3124916 -0.3202062 -1.48528929 1.2937873
## 4672 -0.2983763 -0.310694 3.1998377 -0.3202062 1.45056099 1.2937873
## 28324 -0.2983763 -0.310694 -0.3124916 3.1227451 -1.48528929 1.2937873
## 28701 -0.2983763 -0.310694 -0.3124916 3.1227451 -0.01736415 -1.1208203
## 14051 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.01736415 -1.1208203
## 26578 -0.2983763 -0.310694 -0.3124916 -0.3202062 -0.75132672 -0.5171684
## day.C day^4 day^5 day^6 is_major_gdpNot Major
## 22546 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 29151 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 18166 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 20108 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 21654 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 10433 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 11330 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 10690 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 19147 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18225 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 30110 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 27870 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 27498 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 19997 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 17368 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 20599 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 14673 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 24600 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 8447 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 27908 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 12890 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 28529 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 2533 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 24376 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 6241 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 564 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 20961 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 21541 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 13045 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 13215 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 13 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 9141 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 2979 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 4648 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 4671 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 31959 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 7316 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 13792 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 20140 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 1565 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 26701 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16556 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 18104 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 11927 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 9144 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 26961 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 32960 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 1527 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 14725 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 29171 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1634 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 12257 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 32905 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 15486 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 26180 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 1571 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 8986 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 22031 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 20038 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 26910 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 31831 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 15755 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 1109 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 19312 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 32657 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 29357 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 786 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 205 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 16246 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 304 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 23894 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 10046 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 8135 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
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## 30912 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 30271 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 4267 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 13610 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 11448 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 20580 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 19659 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 18694 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 4560 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 27525 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 4850 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 26882 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 12047 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 6009 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 14304 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 20906 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 29701 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 7896 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 4314 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 182 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 24072 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 23514 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 7988 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 22230 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 28548 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 17266 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 4992 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 28311 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 32130 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16555 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 13112 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 8512 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 21423 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 1351 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 24579 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 16841 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 21322 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 10222 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 30540 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 8141 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 17289 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 5551 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 6263 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1461 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 11417 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1820 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 910 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 13932 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 22830 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 11028 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 4509 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 27739 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 14806 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 29144 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 11281 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 15296 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 393 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 1231 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 23821 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 15359 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 28281 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 1340 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 6861 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 4511 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 19540 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 1364 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 30479 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 27183 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 20253 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 16593 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 12460 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 13328 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 27369 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 18795 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 1988 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 8530 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 6637 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 10798 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 8563 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 3713 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 25752 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 29102 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 13660 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 14985 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 12203 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 10146 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 4000 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 33049 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 20895 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 10302 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 11539 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 12556 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 26056 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 26127 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 13645 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 31945 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 9061 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 16010 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 8736 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 9958 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 11617 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 12010 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 12514 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 2974 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 9668 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16044 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 11655 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 6818 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 29018 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 4341 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 23949 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 26561 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18863 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 20650 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 4730 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 25485 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 16381 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18004 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 24164 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 25422 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 9266 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 5937 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 11357 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 1275 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 3169 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 30457 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 27615 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 25322 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 6970 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 8939 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 12696 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 30693 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 32975 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 30476 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 5755 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16948 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 25129 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 3905 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 4920 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 19358 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 13988 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 7870 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 16861 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 9674 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 29381 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 19042 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 1839 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 4432 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 1448 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 30273 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 24289 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 4262 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 23846 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 4941 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 5026 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6726 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 16663 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 17181 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 1214 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 19686 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 16156 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 8757 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 24439 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 32011 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 6767 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 27998 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 16357 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 27216 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 8990 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 5581 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 15222 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 1593 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 20855 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16585 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 30644 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 23928 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 30429 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 16029 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 5885 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 15184 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 3808 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 19756 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 24957 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 9606 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 22763 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 17533 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 887 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 17211 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 27957 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 22787 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 8958 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 15347 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 7462 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 25279 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 2549 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 25015 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 25716 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 22876 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 17981 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 24497 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 23935 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 3165 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 32458 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 31076 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 22020 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 12311 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 24501 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 725 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 30646 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 24203 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 10240 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 4955 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 24034 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 12002 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 25509 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 15923 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 15532 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 30306 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 27674 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 15118 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 16215 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 8099 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 11757 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 17193 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 16888 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 20051 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 7384 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 26612 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 26923 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 31873 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 9 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
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## 5110 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 10103 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 3969 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 26445 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 25796 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
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## 15429 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 3594 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 3788 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 25007 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 19732 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 28695 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 3581 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 20752 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 16972 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 15679 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 6098 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 12787 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 30904 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 31405 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 12461 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 5485 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
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## 18984 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 8017 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 7023 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 26978 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 3904 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16266 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 9390 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 16600 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 3613 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 10172 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 13685 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6096 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 11057 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 27510 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 5073 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 23274 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 24344 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 2104 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 15040 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 20326 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 846 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 20655 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 15383 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 22522 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 17565 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 11812 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 8154 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 22851 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 23240 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 32185 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 32048 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 23295 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 16536 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 3281 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 8113 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 20946 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 24640 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 7044 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 6872 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 4280 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 29605 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
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## 14458 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 26877 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 30396 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 6013 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 30988 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 28649 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 3235 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 11899 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 20546 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 15507 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 1967 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 7364 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 4929 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 11356 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 28015 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 22677 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 31248 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 19644 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 19930 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 28929 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 14583 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 13739 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 13507 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 9671 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 18316 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 26919 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 17470 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 16469 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 13087 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 30301 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 15531 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 19114 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1520 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 3763 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 28410 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 21290 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 2712 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 17924 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 26604 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6518 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 6723 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 30753 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 14438 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 30095 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 26753 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 30845 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 14240 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6008 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 4187 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 30066 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 26414 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 10785 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 18005 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 23035 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 2765 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 4521 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 22877 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 27589 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 28620 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6253 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7126 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 11114 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 10782 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 20032 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 27888 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 20851 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 30486 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 13356 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 4968 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 21451 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 20883 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 32663 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 32363 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 17191 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 24440 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 27199 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 26185 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 2852 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 10488 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 658 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 11369 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 16601 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 6865 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 27152 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 24794 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 24999 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1623 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 20510 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 2298 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 22499 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 755 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 24986 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 8334 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 18441 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 12128 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 24712 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 13572 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 32328 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 3772 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 5457 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 31220 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 4079 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 2770 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 8945 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 21357 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18341 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 21268 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 15742 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 4951 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 26397 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 13273 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 9809 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 23985 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 29888 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 11597 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 14989 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 21904 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1018 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 30334 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 6871 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 13793 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 8029 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 6193 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 9965 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 7445 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 5418 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7399 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 10634 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 19517 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 1166 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 5742 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 12428 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 15156 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 10685 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 2825 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 32641 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 31421 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 3017 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 13157 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 22847 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18231 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 18496 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 7373 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 7172 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 6102 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 32761 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 17613 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 16450 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 21876 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 20011 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 27478 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 13390 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 20787 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 9316 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 5694 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 30685 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 24653 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 12578 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 8837 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 6522 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 3342 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 17143 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 19832 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 32202 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 9696 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 9924 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 31733 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 14761 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 10270 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 9796 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 144 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
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## 29254 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 12522 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
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## 5829 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 5879 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 3767 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 26313 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 15791 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 29264 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 15024 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7091 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 23662 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
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## 8063 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 1382 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 20383 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 2779 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 7649 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 26211 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 9791 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
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## 12658 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 11905 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6729 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 4156 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 33217 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 30062 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 11769 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 28691 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7755 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 26565 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18717 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 21937 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 15695 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 28394 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 14339 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 18229 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1916 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 6312 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 14896 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 8900 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 25649 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 12469 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 25786 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 20426 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 21136 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 17644 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 17327 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 28999 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 24453 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 30171 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 18663 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 5817 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 31302 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 6890 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 1935 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 31346 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18998 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 14178 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 20424 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 21434 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 29251 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 26967 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## 18380 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 17592 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 12159 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 28958 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 21925 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 5396 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 28615 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 15845 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 25447 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 32773 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 9116 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 17795 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 6475 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1342 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7569 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 1910 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 2306 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 29427 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 29668 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 27591 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 28731 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 2377 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 22084 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 20208 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 1167 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 26884 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 8164 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 19860 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 28800 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 25827 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 1689 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 14334 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 23830 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 22919 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 27491 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 1134 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
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## 10310 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 8457 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 16794 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 6568 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 7162 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 21967 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 24115 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 25480 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 12713 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 26166 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 32859 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 14288 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 27699 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 30877 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 826 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 23595 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 28770 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 17418 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 13622 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 23546 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7890 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
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## 14732 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 5588 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 24839 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16476 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 11490 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 6351 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 29684 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 23753 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 21179 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 26274 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 23149 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 32334 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 20081 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 11088 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18392 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 25678 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 23968 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 26981 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 6512 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 20100 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 14325 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 5710 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 9197 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 31441 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 27775 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 16653 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 4154 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 19981 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 32794 1.134501998 -1.231512 1.06142901 -0.3905665 -1.3239336
## 17962 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 8482 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 8053 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 891 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 22454 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 29474 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 29960 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 26415 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 14079 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 29256 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 66 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 15983 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 11199 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 19773 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 2349 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 27774 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 29012 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 16394 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 21577 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 19374 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 18197 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 9251 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 19587 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 30977 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 10532 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 32795 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 25503 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 29590 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 3832 -0.002823022 1.332101 0.06188972 -1.3901066 -1.3239336
## 5012 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 32994 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 9885 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 5614 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 29725 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 24280 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 31125 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 10767 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 12792 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 8475 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 16865 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 27577 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 25572 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 151 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 32652 1.134501998 0.346096 -1.18753439 1.1087436 -1.3239336
## 25887 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 2223 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 19433 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 33086 -1.140148043 -1.231512 -0.93764957 -0.3905665 -1.3239336
## 6992 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 9943 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 23683 -1.140148043 0.346096 1.31131383 1.1087436 0.7552663
## 11260 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 33006 1.134501998 0.346096 -1.18753439 1.1087436 0.7552663
## 7940 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 4806 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 4407 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 8697 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 21670 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 5016 -1.140148043 0.346096 1.31131383 1.1087436 -1.3239336
## 15078 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
## 26433 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 13317 -0.002823022 1.332101 0.06188972 -1.3901066 0.7552663
## 4044 -1.140148043 -1.231512 -0.93764957 -0.3905665 0.7552663
## 9410 1.134501998 -1.231512 1.06142901 -0.3905665 0.7552663
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## [ reached getOption("max.print") -- omitted 11354 rows ]#library(plyr)
#l_dummy<- rename(l_dummy, c("beta"="two", "gamma"="three"))
#names(l_dummy)[names(l_dummy)=="scorex< 600"] <- "scorexLess"
train_data_indicies <- sample(1:nrow(listing_rand),
replace = F,
size = floor(nrow(listing_rand) * 0.8))
train_data <- listing_rand[train_data_indicies, ]
test_data <- listing_rand[-train_data_indicies, ]
#listings_random <- listings[sample(nrow(listings)),]
round(prop.table(table(train_data$loan_status)) * 100, 1)##
## 0 1
## 67 33round(prop.table(table(test_data$loan_status)) * 100, 1)##
## 0 1
## 65.6 34.4#listing_formula <- colnames(train_data) %>%
# {paste(.[! . %in% "loan_status"], collapse = " + ")} %>%
# paste("loan_status ~ ", .) %>%
# as.formula()accuracy function
accuracy <- function(predicted, trueval, model, hideoutput = F) {
stopifnot(length(predicted) == length(trueval))
result <- sum(predicted == trueval) / length(predicted)
if (!hideoutput) {cat("Model:", model, "had", result, "accuracy\n")}
return(result)
}